The sericulture industry heavily depends on the quality and health of mulberry leaves, the primary food source for silkworms. Accurate classification and disease detection of mulberry leaves are crucial for ensuring high-quality silk production. Traditional methods for leaf classification are challenged by complex leaf features, environmental factors, and the similarity of symptoms across different diseases, which often lead to misclassification and inefficient disease management. This study introduces BerryNet, a system comprising various types of blocks designed for efficient classification of mulberry leaves and disease detection. We have proposed a new advanced block (Nobel Block) that helps extract the features of our model. The model’s explainability is further enhanced by utilizing Explainable Artificial Intelligence (XAI) techniques, which provide insights into the decision-making process and ensure transparency. We introduced the Adaptive Synergistic Loss Function (ASLF), which simultaneously addresses class imbalance, overconfidence, and poor feature separation in a single loss function, leading to more robust and accurate multi-class classification. Experimental results demonstrate that BerryNet outperforms existing models, achieving test accuracies of 98.67% for leaf classification and 99.09% for disease detection, significantly improving the sustainability and productivity of the sericulture industry. The model’s efficiency and explainability make it a practical tool for real-world applications, particularly in resource-constrained environments.

ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN), Bayesian networks (BN), and reinforcement learning (RL). A PRISMA‐style protocol is used to extract evidence, test across popular corpora, and test models in zero‐day faithful regimes, time‐split, and cross‐dataset transfer. In addition to aggregate accuracy and F1, we also highlight operating‐point reporting the true‐positive rate at a fixed false‐positive rate, ranking measures in the presence of class imbalance, and calibration of probability predictions as a measure of expected error probabilistic calibration, which may include syntactic measures such as time‐to‐alert, throughput, and memory compute footprint. Reported results suggest that DNNs demonstrate the aggregate performance on richly feature inputs (nearly 99.56% accuracy on CICDDoS2019), CNNs on tensorized flows/bytes with advantageous latency at the edge 92.17% on Bot‐IOT), BN provides interpretable uncertainty with acceptable accuracy (99.74% on NSL‐KDD), and RL shows promise as an adaptive detection‐response when there are rewards and safe training environments (96.18% on CSE‐CIC‐IDS2018). We unify the heterogeneity of our datasets and suggest a coherent, leakage‐wary evaluation environment to facilitate comparability and reproducibility. Language or code models of logs and transformer traffic encoders, along with lightweight backbones of edge IDS, become available as subjects of future head‐to‐head studies under equal protocol conditions. The review provides tactical advice on model‐data fit, operating points, calibration, and latency budgets, the precursor to deployment ready, adaptive defence against unknown attacks.

Timely and accurate identification of brain tumors is crucial for optimizing patient outcomes, guiding surgical planning, and determining appropriate treatment strategies. Despite the advancements in MRI-based tumor detection and artificial intelligence, developing reliable and clinically applicable models remains challenging, particularly in contexts where robustness, interpretability, and consistency are critical. Existing approaches often lack advanced 3D imaging capabilities and robust Explainable AI (XAI) techniques, which limit their diagnostic utility and clinical adoption. To address these limitations, this study proposes a robust deep learning architecture that incorporates the improved EfficientNet B7, Xception, and ResNet152 architectures. We further proposed a novel classification model incorporating enhanced data augmentation and histogram equalization to automatically classify brain MRI images into four diagnostic categories. Three distinct datasets, one segmented dataset, and a merged dataset combining all sources were used to train and evaluate the models. The proposed models achieved accuracies in the range of 97–99%, demonstrating consistent and strong performance across datasets. To enhance diagnostic transparency, XAI techniques, including Grad-CAM and LIME, were employed to provide visual insights into the system’s decision-making processes. Additionally, this study incorporated a novel 3D reconstruction approach across the sagittal, coronal, and axial planes, providing a comprehensive view that supports improved diagnostic accuracy and precise clinical interpretation. Overall, this study demonstrates a novel unified framework integrating classification, segmentation, XAI, current gaps in brain tumor diagnostics and holding significant potential to enhance clinical reliability, transparency, and precision, ultimately improvinge clinical reliability, interpretability, and precision, ultimately supporting better patient outcomes.

Deep learning-based automated systems have emerged as powerful tools for medical image analysis. However, existing models often face limitations when applied to gastrointestinal (GI) tract imaging and segmentation because the irregular organ shapes, varying sizes, overlapping regions, and low-contrast boundaries significantly affect the performance and generalizability of current segmentation techniques. To address this challenge, we propose PTransNet, a novel transformer-based segmentation network for GI images. Here, we introduce a novel technique, Contextual Relative Positional Encoding (CRPE), that explicitly embeds relative spatial relationships among features, thereby improving the network’s spatial reasoning in anatomical scenes. We proposed the PTransNet architecture, which combines UNet and a transformer with a novel CRPE for optimized segmentation. The encoder-decoder architecture of UNet enhances model efficiency by capturing contextual features through downsampling and precisely localizing them via symmetric upsampling with skip connections. Using the UW-Madison GI Tract dataset (16,590 images), PTransNet is trained, validated, and tested (80%-10%-10% split) to target the segmentation of the three major GI components (small, large, and stomach), which achieved substantially higher testing performances with a Dice score of 93.49%, an IoU (Intersection-over-Union) of 90.51%, and a specificity of 99.87%. Moreover, PTransNet achieved 10.64% and 13.4% gains in dice and IoU scores, respectively, when using the novel CRPE component, highlighting its contribution to precision diagnosis. The model also outperformed state-of-the-art methods across higher MCC, BM, HD95, NSD, MASD, precision, recall, and F1 scores, demonstrating robust segmentation quality. These advances highlight PTransNet’s strength in handling the challenges of spatial scenarios. Thus, PTransNet has substantial practical implications for the reliable delineation of GI organs in clinical workflows (e.g., radiotherapy planning), thereby contributing to improved healthcare outcomes in the management of GI disease.

The diagnosis of glioma, a complex and often deadly brain tumor, involves extensive medical examinations. Still, accurately grading and classifying gliomas is difficult, as different areas within the same tumor can exhibit varying characteristics. The integration of radiomics, a clinically relevant feature extraction method, with machine learning (ML) is becoming increasingly popular in addressing this issue, but several research gaps persist. To this end, this study proposes a novel deep neural network, RGNN3D, that combines Graph Neural Networks with LSTM layers to precisely grade gliomas in 3D magnetic resonance imaging (MRI) data. To train our proposed model, we meticulously extracted 112 radiomic biomarkers. Utilizing the biomarkers, RGNN3D constructs a graph, channels essential information through its layers, and preserves only pertinent information via its integrated memory cells. The proposed framework attained an accuracy of 98.58%, aligning with the performance of previous state-of-the-art architectures and surpassing prior radiomic-based ML models. We further employed an explainable AI approach (LIME) to highlight the most significant features, assisting radiologists in making more informed decisions. In short, RGNN3D offers a reliable and robust computer-aided solution for potential clinical application in the automated identification of gliomas.

Major Depressive Disorder (MDD) is one of the most prevalent and severe psychiatric conditions that pose significant diagnostic challenges due to the complex and subtle nature of brain dynamics. Conventional EEG analysis methods often fall short in capturing these nuanced temporal–frequency dynamics. To address this limitation, we propose a deep learning framework that integrates Continuous Wavelet Transform (CWT), scalogram representations, and transfer learning with the VGG16 architecture. The rationale for employing CWT and scalograms lies in their ability to preserve both time and frequency information, enabling the extraction of clinically meaningful features of brain dynamics, while transfer learning with VGG16 leverages prior knowledge from large-scale image datasets, improving feature generalization and reducing the computational burden of training on limited EEG data. By combining these techniques with robust preprocessing and channel selection, the proposed model effectively identifies distinct spectral power alterations, particularly in theta and alpha bands, associated with MDD. The experimental results demonstrate that the proposed model outperformed traditional methods, achieving a more precise detection of MDD. These findings highlight the promise of combining advanced signal processing with deep learning for more reliable, non-invasive diagnosis of MDD through EEG signals, and support its clinical application.

• Federated learning framework for privacy-preserving EEG-based MDD detection • Gamma-band EEG modeling captures discriminative depression patterns • Signal-aware 1D-CNN enables stable learning under non-IID federated data • Comparative evaluation of FedAvg, FedNova, and ensemble aggregation • Low latency supports real-time clinical EEG inference and deployment The high prevalence of Major Depressive Disorder (MDD) underscores the need for innovative diagnostic methods that are both accurate and respectful of patient privacy. This study addresses the challenge of accurately predicting MDD from distributed patient data while maintaining data confidentiality. We propose a two-step diagnostic framework: first, it analyzes EEG signals to detect unusual patterns, and then it uses a web-based system leveraging federated learning combined with a Convolutional Neural Network (CNN) for the final prediction of MDD. A key contribution of this research is the introduction of a weighted model averaging algorithm that enhances traditional federated learning by incorporating client-specific loss values, thereby improving model performance. The system is developed using the Django framework, ensuring robustness and scalability. Experimental results show an accuracy of 87.48%, a precision of 81.87%, a recall of 97.90%, and an Area Under the Curve (AUC) of 96.86%. These results demonstrate the potential of our federated learning approach for accurate and private prediction of MDD. By leveraging EEG signals, this approach provides a non-invasive, reliable diagnostic tool for early detection of MDD, with significant potential for clinical practice. The findings of this study contribute to neuropsychiatric research and support the development of privacy-preserving machine learning systems in healthcare.

Protein toxicity prediction is crucial for drug discovery, safety assessment, and toxicological research. This study introduces $\mathrm{Deep\_{T}PPred}$, a novel hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for accurate protein toxicity prediction. The model effectively combines diverse protein sequence descriptors to capture complex sequence relationships by leveraging a feature fusion technique. The methodology involved advanced feature extraction, rigorous training, and performance evaluation using benchmark datasets. $\mathrm{Deep\_{T}PPred}$ demonstrates state-of-the-art performance with an accuracy of 0.9983, specificity of 0.9988, sensitivity of 0.9975, and Kappa and MCC values of 0.9963. These results underscore the proposed model's robustness, reliability, and generalization capability, surpassing existing models across all metrics. The study highlights the potential of hybrid DL and feature fusion techniques to significantly enhance protein toxicity prediction, providing valuable insights and tools for bioinformatics pipelines and applications.

Viruses are a significant threat to human life, as demonstrated by the global COVID-19 pandemic and the Ebola outbreak. Diseases such as smallpox, AIDS, hepatitis, liver cancer, and cervical cancer often caused by the Human Papillomavirus can lead to fatal outcomes. Over the years, extensive research has focused on developing vaccines and antiviral drugs, which have successfully contained and, in some cases, eradicated viral infections. Recently, computational techniques, particularly machine learning algorithms, have made notable progress in identifying potential Antiviral Peptides (AVPs), thereby accelerating experimental validation for therapeutic applications. This study introduces machine learning techniques and a Light Gradient Boosting Machine (LGBM) model combined with three feature-encoding methods to predict whether a protein sequence contains effective antiviral peptides. Eight machine learning algorithms were evaluated using both single-feature and combined feature encodings. Among them, the lightweight LGBM model trained on combined encoded features achieved the best performance, with an accuracy of 98%, precision of 97%, recall of 98%, F1-score of 98%, and an AUC of 1.00. Compared to existing models, the proposed approach achieved approximately 2% higher accuracy using individual encoding methods and about 3% higher accuracy with combined features. The reliability and effectiveness of the proposed model highlight its potential value for pharmaceutical development and academic research.

Despite the advancements in quantum convolution or quanvolution, challenges persist in making quanvolution scalable, efficient, and applicable to multi-dimensional data. Existing quanvolutional networks heavily rely on classical layers, with minimal quantum involvement due to inherent limitations in current quanvolution algorithms. Moreover, the application of quanvolution in the domain of 1D data remains largely unexplored. To address these limitations, we propose a new quanvolution algorithm-Quanv1D-capable of processing arbitrary-channel 1D data, handling variable kernel sizes, and generating a customizable number of feature maps, along with a classification network-fully quanvolutional network (FQN)-built solely using Quanv1D layers. Quanv1D is inspired by the classical Conv1D and stands out from the quanvolution literature by being fully trainable, modular, and freely scalable with a self-regularizing feature. To evaluate FQN, we tested it on 20 UEA and UCR time series datasets, both univariate and multivariate, and benchmarked its performance against state-of-the-art convolutional models (both quantum and classical). We found FQN to outperform all compared models in terms of average accuracy while using significantly fewer parameters. Additionally, to assess the viability of FQN on real hardware, we conducted a shot-based analysis across all the datasets to simulate statistical quantum noise and found our model robust and equally efficient.

Ovarian cancer (OC) is a highly lethal malignancy worldwide, necessitating the identification of key genes to uncover its molecular mechanisms and improve diagnostic and therapeutic strategies. This study utilized statistical and machine learning approaches to identify key candidate genes for OC. Three microarray datasets were obtained from the gene expression omnibus database, and analysis began with normalization and differential gene expression analysis using the Limma package. Highly discriminative differentially expressed genes (HDDEGs) were identified through a support vector machine-based approach, yielding 84 overlapping HDDEGs across the datasets. Enrichment analysis of HDDEGs was conducted using DAVID. A protein-protein interaction network constructed via STRING pinpointed central hub genes using CytoHubba metrics. Significant modules were analyzed with molecular complex detection, identifying 18 central hub genes, 11 hub module genes, and 54 meta-hub genes. The intersection of these three gene sets revealed eight shared key genes (FANCD2, BUB1B, BUB1, KIF4A, DTL, NCAPG, KIF20A, and UBE2C). Weighted gene co-expression network analysis identified key modules linked to clinical traits and confirmed grouping eight key candidate genes into a single cluster. These genes were validated using two independent datasets (GSE38666 and TCGA-OC), with area under the curve and survival analyses underscoring their predictive and prognostic significance in OC. This integrative approach advances understanding of OC's molecular basis, identifies potential biomarkers, and emphasizes the clinical relevance of the eight key candidate genes for OC diagnosis, prognosis, and treatment.

Federated learning (FL) is a privacy-preserving paradigm in distributed machine learning that enables clients to collaboratively train models without sharing their raw data. However, the variety of client data, device setups, and network conditions provides serious difficulties for FL systems. Random client sampling in such environments often leads to suboptimal outcomes, including lower model accuracy, slower convergence rates, and reduced fairness. To address these issues, this study proposes a dynamic client selection mechanism based on a scoring system that evaluates clients based on three key parameters: accuracy, loss, and execution time. We propose a scoring-based framework for adaptive client selection in federated learning (FL) and implement it in an ML-driven diabetes detection system. Evaluations with 200 communication rounds demonstrate improved global and local model performance, faster convergence, and optimized resource utilization. The framework dynamically selects clients, improving execution efficiency and addressing key FL challenges, including data heterogeneity, fairness, communication overhead, and privacy. Our findings highlight its potential for scalable and efficient FL in healthcare applications while paving the way for future advancements in adaptive client selection.

The rapid advancement of technology has led to notable changes in the current educational system. Nevertheless, there are still relatively few assisting aids that can help in teaching individuals with disabilities, such as those who are blind or visually impaired. An effective teaching strategy for those who are blind or visually impaired is braille. Although it has been digitized to produce an electronic version, handwritten characters are not considered in those versions. Studies on English character recognition have shown high accuracy, which is not the case with Bangla character recognition. We present an automated system that converts handwritten Bangla characters to braille using novel hybrid deep neural network architectures. Our approach begins with a Character Quality Assessment Framework (CQAF), which employs adaptive thresholds and comprehensive quality metrics designed explicitly for Bangla script characteristics. Building upon this foundation, we present two architectures. HybridNet-L represents our initial multi-stream design, while HybridNet-S is a redesigned lightweight variant that reduces parameters and achieves superior accuracy, making it the primary contribution of this work. To complete the system, we implement a comprehensive accessibility solution featuring real-time braille hardware interface and text-to-speech capabilities. The model effectively processes all 84 Bangla character classes including vowels, consonants, numerics, and compound characters. Extensive evaluation against seven baseline models demonstrates that our HybridNet-S achieves superior performance with 95.80% validation accuracy while maintaining computational efficiency suitable for embedded deployment. Statistical validation and ablation studies confirm the robustness and effectiveness of our multi-stream architecture for practical assistive technology applications.

The deployment of complex deep learning models in resource-limited clinical settings remains a critical challenge for brain tumor classification. This study explores knowledge distillation (KD) and ensembling strategies to enhance brain tumor classification using lightweight convolutional neural networks (CNNs), targeting resource-constrained environments. The research investigates both single-teacher and multi-teacher KD frameworks, utilizing VGG16 and Xception as teacher models to impart knowledge to smaller student models, including Tiny ResNet, Tiny Xception, and Tiny DenseNet. Student models, such as Student-1 (Tiny ResNet), feature a parameter reduction of approximately 83x and 117x times compared to VGG16 and Xception. Moreover, ensembling methods, including Average Ensembling and Weighted Ensembling, were employed to further refine predictive performance further. The multi-teacher KD approach achieved notable accuracy, reaching 96.19 % on Dataset-1 and 95.57 % on Dataset-2, while ensembling techniques yielded up to 98.02 % and 98.15 % for Average Ensembling and Weighted Ensembling, respectively. Furthermore, explainable AI (XAI) techniques, such as Grad-CAM and SHAP, were implemented to improve the interpretability of deep learning models and provide clinicians with insights. The results underscore the potential of combining KD and ensembling to develop robust and efficient AI-driven solutions for brain tumor classification, balancing accuracy, interpretability, and computational efficiency. These advancements make it possible to develop brain tumor classification systems that are accurate and efficient enough for deployment in resource-constrained environments.

Lung cancer, one of the most prevalent and deadliest diseases, necessitates early detection for patient survival. The low level of contrast between lesions and adjacent lung tissue, coupled with the diverse shapes and structures of lung nodules, poses significant challenges for their accurate identification and classification. Despite the extensive use of machine learning methods, the lack of adequately annotated datasets significantly hampers efficient model training. Moreover, the lack of transparency in deep learning models has led to their perception as “black boxes”, constraining their credibility for end users like radiologists. To address these issues, we present LungCT-NET , a novel transfer learning-based architecture coupled with ensemble learning and explainable AI for binary classification of lung nodules into malignant and benign using lung CT scans. LungCT-NET incorporates essential preprocessing, reconfiguring transfer learning models, and an advanced stacking ensemble strategy utilizing combinations of the top-performing pre-trained models. Several transfer learning algorithms are employed, including VGG-16, VGG-19, MobileNet-V2, InceptionNet-V3, EfficientNet-B0, ResNet152-V2, and DenseNet-121. Extensive experimental analyses have been carried out on the LIDC-DIRI dataset using various performance metrics for evaluation. The findings demonstrate that the suggested framework significantly exceeds state-of-the-art approaches, achieving an accuracy, precision, F1 score and recall of 98.99%, an AUC of 98.15%. Finally, the integrated SHapley Additive exPlanations (SHAP) enhance the grasp of model outcomes, hence increasing confidence in lung cancer prognosis. Therefore, the proposed innovative LungCT-NET can potentially support clinical settings by automating lung nodule classification from low-dose CT scans, aiding physicians and radiologists in prompt, accurate diagnoses.

Traffic accidents are unexpected incidents where one or multiple vehicles collide and damage properties, dying or injuring many individuals. It causes significant social burdens, including loss of life, serious injuries, and economic suppression from medical costs, property damages, and productivity losses. This kind of incident brings a miserable situation for the affected people. Many factors, including infrastructure, weather, vehicles, or driver-related issues, contribute to happening traffic accidents. This work explores an innovative approach by investigating contributing factors to ensure road safety. In this study, an ensemble machine learning model, namely Weighted Fusion-Based Feature Selection (WFFS), was proposed to identify different significant features to reduce the effects of traffic accidents. A large amount of traffic accident records from the United Kingdom (UK) were gathered and split into several folds, which were cleaned and balanced using different techniques such as removing percentages, Synthetic Minority Oversampling Technique (SMOTE), and random oversampling. Then, WFFS were employed in each fold and identified the most significant features to predict traffic accident severity more accurately. Different classifiers, such as tree-based, bagging, boosting, and voting classifiers, were implemented into WFFS-generated feature subsets and performed better than primary data and other feature subsets. In this case, the random tree-based bagging method provided the highest accuracy of 97.28% to predict accident severity for the WFFS subset, where its number of features is 18. However, different classifiers achieved better accuracies for 6 out of 11 times using WFFS. This method is highly recommended for policymakers and transportation engineers to identify potentially hazardous locations and take appropriate measures to diminish the effects of traffic accidents.

Methicillin-resistant S. aureus (MRSA) has prominently emerged among the recognized causes of community-acquired and hospital infections. We proposed a novel computational approach, iMRSA-Fuse, based on a multi-view feature fusion strategy for fast and accurate anti-MRSA peptide identification. In iMRSA-Fuse, we explored and integrated 12 different sequence-based feature descriptors from multiple perspectives, in conjunction with 12 popular machine learning (ML) algorithms, to construct multi-view features that were able to fully capture the useful information of anti-MRSA peptides. Additionally, we applied our customized genetic algorithm to determine a set of multi-view features to enhance its discriminative ability. Based on a series of comparative results, our multi-view features exhibited the most discriminative ability compared to several conventional feature descriptors. Moreover, concerning the independent test dataset, iMRSA-Fuse achieved the best balanced accuracy (BACC) and Matthew's correlation coefficient (MCC) of 0.997 and 0.981, respectively with an increase of 3.93 and 7.78%, respectively. Finally, to facilitate the large-scale identification of candidate anti-MRSA peptides, a user-friendly web server of the iMRSA-Fuse model is constructed and is freely accessible at https://pmlabqsar.pythonanywhere.com/iMRSA-Fuse. We anticipate that this new computational approach will be effectively applied to screen and prioritize candidate peptides that might exhibit the great anti-MRSA activities.

Modern healthcare should include artificial intelligence (AI) technologies for disease identification and monitoring, particularly for chronic conditions, including heart, diabetes, kidney, liver, and thyroid. According to the World Health Organization (WHO), heart, diabetes, and liver diseases (hepatitis B and C and liver cirrhosis) are leading causes of mortality. The prevalence of thyroid and chronic kidney diseases is also increasing. We conducted a comprehensive review of the available literature to assess the current state of AI advancement in disease diagnosis and identify areas needing further attention. Machine learning (ML), deep learning (DL), and ensemble learning (EL) approaches have gained popularity in recent years due to their excellent results across various medical domains. This study focuses on their application in disease diagnosis and monitoring. We present a framework designed to provide aspiring researchers with a foundational understanding of popular algorithms and their significance in disease identification. Additionally, we highlight the importance of blockchain technology in the healthcare industry for safeguarding patient data confidentiality and privacy. The decentralized and immutable nature of blockchain can enhance data security, promote interoperability, and empower patients to control their medical information. By demonstrating the potential of advanced ML methods and blockchain technology to transform healthcare systems and improve patient outcomes, our research contributes to the field of disease diagnostics.

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain examination and advances in AI-based detection methods, building an accurate and efficient model for detecting and categorizing tumors from MRI images remains a challenge. To address this problem, we proposed a deep Convolutional Neural Network (CNN)-based architecture for automatic brain image classification into four classes and a U-Net-based segmentation model. Using six benchmarked datasets, we tested the classification model and trained the segmentation model, enabling side-by-side comparison of the impact of segmentation on tumor classification in brain MRI images. We also evaluated two classification methods based on accuracy, recall, precision, and AUC. Our developed novel deep learning-based model for brain tumor classification and segmentation outperforms existing pre-trained models across all six datasets. The results demonstrate that our classification model achieved the highest accuracy of 98.7% in a merged dataset and 98.8% with the segmentation approach, with the highest classification accuracy reaching 97.7% among the four individual datasets. Thus, this novel framework could be applicable in clinics for the automatic identification and segmentation of brain tumors utilizing MRI scan input images.

Abstract Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for the development of diagnostic aids to facilitate this. A lightweight Involutional Neural Network (INN) architecture has been developed to diagnose ASD. The model follows a simpler architectural design and has less number of parameters than the state‐of‐the‐art (SOTA) image classification models, requiring lower computational resources. The proposed model is trained to detect ASD from eye‐tracking scanpath (SP), heatmap (HM), and fixation map (FM) images. Monte Carlo Dropout has been applied to the model to perform an uncertainty analysis and ensure the effectiveness of the output provided by the proposed INN model. The model has been trained and evaluated using two publicly accessible datasets. From the experiment, it is seen that the model has achieved 98.12% accuracy, 96.83% accuracy, and 97.61% accuracy on SP, FM, and HM, respectively, which outperforms the current SOTA image classification models and other existing works conducted on this topic.

Abstract Quick and accurate diagnosis of COVID‐19 is crucial in preventing its transmission. Chest X‐ray (CXR) imaging is often used for diagnosis, however, even experienced radiologists may misinterpret the results, necessitating computer‐aided diagnosis. Deep learning has yielded favourable results previously, but overfitting, excessive variance, and generalization errors may occur due to noise and limited datasets. Ensemble learning can improve predictions by using robust techniques. Therefore, this study, proposes two‐fold strategy that combines advanced and robust algorithms, including DenseNet201, EfficientNetB7, and Xception, to achieve faster and more accurate COVID‐19 detection. Segmented lung images were generated from CXR images using the residual U‐Net model, and two attention‐based ensemble neural networks were used for classification. The COVID‐19 radiography dataset was used to evaluate the proposed approach, which achieved an accuracy of 98.21%, 93.4%, and 89.06% for two, three, and four classes respectively which outperformed previous studies by a significant margin considering COVID, viral pneumonia, and lung opacity simultaneously. Despite the similarity in CXR images of COVID, pneumonia, and lung opacity, the proposed approach achieved 89.06% accuracy, demonstrating its ability to recognize distinguishable features. The developed algorithm is expected to have applications in clinics for diagnosing different diseases using X‐ray images.

Brain tumors are among the most fatal and devastating diseases, often resulting in significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to devise treatment plans that can extend the lives of affected individuals. Manually identifying and analyzing large volumes of MRI data is both challenging and time-consuming. Consequently, there is a pressing need for a reliable deep learning (DL) model to accurately diagnose brain tumors. In this study, we propose a novel DL approach based on transfer learning to effectively classify brain tumors. Our novel method incorporates extensive pre-processing, transfer learning architecture reconstruction, and fine-tuning. We employ several transfer learning algorithms, including Xception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments used the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2, InceptionResNetV2, and DenseNet201, respectively. Our findings reveal that ResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRI brain tumor dataset, outperforming existing models. Therefore, our proposed model’s ability to accurately classify brain tumors in a short timeframe can aid neurologists and clinicians in making prompt and precise diagnostic decisions for brain tumor patients.

Abstract Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.

INTRODUCTION: Most of the typical chemokine receptors (CKRs) have been identified as coreceptors for a variety of human and simian immunodeficiency viruses (HIVs and SIVs). This study evaluated CCRL2 to examine if it was an HIV/SIV coreceptor. METHODS: The Human glioma cell line, NP-2, is normally resistant to infection by HIV and SIV. The cell was transduced with amplified cluster of differentiation 4 (CD4) as a receptor and CCR5, CXCR4 and CCRL2 as coreceptor candidates to produce NP-2/CD4/coreceptor cells (). The cells were infected with multiplicity of infection (MOI) 1.0. Infected cells were detected by indirect immunofluorescence assay (IFA). Multinucleated giant cells (MGC) in syncytia were quantified by Giemsa staining. Proviral DNA was detected by polymerase chain reaction (PCR), and reverse transcriptase (RT) activity was measured. RESULTS: IFA detected viral antigens of the primary isolates, HIV-1HAN2 and HIV-2MIR in infected NP-2/CD4/CCRL2 cells, indicated CCRL2 as a functional coreceptor. IFA results were confirmed by the detection of proviral DNA and measurement of RT-activity in the spent cell supernatants. Additionally, MGC was detected in HIV-2MIR-infected NP-2/CD4/CCCRL2 cells. HIV-2MIR were found more potent users of CCRL2 than HIV-1HAN2. Moreover, GWAS studies, gene ontology and cell signaling pathways of the HIV-associated genes show interaction of CCRL2 with HIV/SIV envelope protein. CONCLUSIONS: In vitro experiments showed CCRL2 to function as a newly identified coreceptor for primary HIV-2 isolates conveniently. The findings contribute additional insights into HIV/SIV transmission and pathogenesis. However, its in vivo relevance still needs to be evaluated. Confirming in vivo relevance, ligands of CCRL2 can be investigated as potential targets for HIV entry-inhibitor drugs.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

To identify key gene expression pathways altered with infection of the novel coronavirus SARS-CoV-2, we performed the largest comparative genomic and transcriptomic analysis to date. We compared the novel pandemic coronavirus SARS-CoV-2 with SARS-CoV and MERS-CoV, as well as influenza A strains H1N1, H3N2 and H5N1. Phylogenetic analysis confirms that SARS-CoV-2 is closely related to SARS-CoV at the level of the viral genome. RNAseq analyses demonstrate that human lung epithelial cell responses to SARS-CoV-2 infection are distinct. Extensive Gene Expression Omnibus literature screening and drug predictive analyses show that SARS-CoV-2 infection response pathways are closely related to those of SARS-CoV and respiratory syncytial virus infections. We validated SARS-CoV-2 infection response genes as disease-associated using Kaplan-Meier survival estimates in lung disease patient data. We also analysed COVID-19 patient peripheral blood samples, which identified signalling pathway concordance between the primary lung cell and blood cell infection responses.

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected individuals that have hypertension or cardiovascular comorbidities have an elevated risk of serious coronavirus disease 2019 (COVID-19) disease and high rates of mortality but how COVID-$19$ and cardiovascular diseases interact are unclear. We therefore sought to identify novel mechanisms of interaction by identifying genes with altered expression in SARS-CoV-$2$ infection that are relevant to the pathogenesis of cardiovascular disease and hypertension. Some recent research shows the SARS-CoV-$2$ uses the angiotensin converting enzyme-$2$ (ACE-$2$) as a receptor to infect human susceptible cells. The ACE2 gene is expressed in many human tissues, including intestine, testis, kidneys, heart and lungs. ACE2 usually converts Angiotensin I in the renin-angiotensin-aldosterone system to Angiotensin II, which affects blood pressure levels. ACE inhibitors prescribed for cardiovascular disease and hypertension may increase the levels of ACE-$2$, although there are claims that such medications actually reduce lung injury caused by COVID-$19$. We employed bioinformatics and systematic approaches to identify such genetic links, using messenger RNA data peripheral blood cells from COVID-$19$ patients and compared them with blood samples from patients with either chronic heart failure disease or hypertensive diseases. We have also considered the immune response genes with elevated expression in COVID-$19$ to those active in cardiovascular diseases and hypertension. Differentially expressed genes (DEGs) common to COVID-$19$ and chronic heart failure, and common to COVID-$19$ and hypertension, were identified; the involvement of these common genes in the signalling pathways and ontologies studied. COVID-$19$ does not share a large number of differentially expressed genes with the conditions under consideration. However, those that were identified included genes playing roles in T cell functions, toll-like receptor pathways, cytokines, chemokines, cell stress, type 2 diabetes and gastric cancer. We also identified protein-protein interactions, gene regulatory networks and suggested drug and chemical compound interactions using the differentially expressed genes. The result of this study may help in identifying significant targets of treatment that can combat the ongoing pandemic due to SARS-CoV-$2$ infection.

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein-protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.

Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.

CytoCom is an interactive plugin for Cytoscape that can be used to search, explore, analyse and visualize human disease comorbidity network. It represents disease-disease associations in terms of bipartite graphs and provides International Classification of Diseases, Ninth Revision (ICD9)-centric and disease name centric views of disease information. It allows users to find associations between diseases based on the two measures: Relative Risk (RR) and [Formula: see text]-correlation values. In the disease network, the size of each node is based on the prevalence of that disease. CytoCom is capable of clustering disease network based on the ICD9 disease category. It provides user-friendly access that facilitates exploration of human diseases, and finds additional associated diseases by double-clicking a node in the existing network. Additional comorbid diseases are then connected to the existing network. It is able to assist users for interpretation and exploration of the human diseases by a variety of built-in functions. Moreover, CytoCom permits multi-colouring of disease nodes according to standard disease classification for expedient visualization.

BACKGROUND: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I'd rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes. RESULTS: We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the ϕ-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases. CONCLUSIONS: Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.

BACKGROUND: This work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure. RESULTS: Since both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way. CONCLUSIONS: We present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformatics.