Antibiotic resistance is a mounting global health challenge, demanding accelerated strategies for discovering new drugs and understanding their mechanism of action (MoA). In this study, we propose a computational framework to predict the MoA of natural antibiotics by leveraging Tanimoto molecular similarity metrics and network-based clustering. A curated dataset of known antibiotics with established MoA's was used to generate a similarity network, into which predicted natural antibiotics were integrated. Using the DPClus algorithm, we identified structurally coherent clusters that frequently aligned with known functional categories. The results suggest that predicted antibiotics sharing structural clusters with characterized compounds are likely to exhibit similar biological activities. Our approach provides an interpretable and scalable method for prioritizing novel antibiotic candidates for further validation, contributing to faster and more informed drug discovery pipelines.

This study employs a multi-faceted approach to identify potential natural antibiotics derived from plants used in the Unani formula. The first approach involves a supervised network analysis that utilizes distance measurement to analyze the interconnectivity of the network data. This approach identifies 26 candidate plants with the potential for natural antibiotics. The second approach involves machine learning techniques, specifically deep learning methods, to extract relevant features from the Unani data. This method produces a list of 29 potential plants that exhibit characteristics of natural antibiotics. Notably, seven plants overlap between the approaches - Piper longum, Trachyspermum ammi carum copticum, Santalum album, Cyperus rotundus, Vitis vinifera, Matricaria chamomilla and Zingiber officinale - shown in the literature to exhibit antibacterial properties direct or indirect. The study employs two analytical methods to comprehensively identify potential plants with natural antibiotic properties from the Unani formula. These findings could have significant implications for developing novel antibiotics to combat antibiotic-resistant pathogens.

In order to address antibiotics resistance, multi-drug resistance, and superbugs phenomena, our research explored the utility of Jamu ingredients on the molecular level to predict new natural antibiotic candidates. Jamu is one of the popular traditional medicines from Indonesia, with different therapeutics usage including curing diseases caused by bacterial infection. We used three types of machine learning methods, such as Random Forest (RF), Support Vector Machine (SVM), Deep Learning (DL), to classify Jamu formulas according to their effectiveness against different types of bacterial diseases. The best accuracy for RF, SVM, and DL models are 89%, 84%, and 80%, respectively. We extracted the potential compounds based on the best model as candidate antibiotics corresponding to five groups of efficacies, e.g., digestive systems, respiratory systems, reproductive systems, skin and soft tissue, and urinary systems. Overall, we mined 111 compounds, and many of them could be validated by published literature, and considering structural similarities with known antibiotics.

Identifying a specific cancer subtype from a variety of candidates is vital for precise and effective treatment. However, cancer subtyping is highly non-trivial as a result of cancer heterogeneity. While significant efforts have been put into understanding the mechanism of cancer subtypes via studying the omics data, existing methods run the risk of presenting biased analyses resulted from overfitting the high-dimensional and scarce omics data. In this paper, we propose a novel generative model that directly models the cancer data distribution by which downstream tasks can circumvent the curse of overfitting and achieve better performance. Unlike conventional generative modeling schemes, the proposed method underlines hierarchical categorical latent spaces to extract global features and local details respectively from transcriptomics and genomics profiles, which is the first to be considered in the cancer subtyping literature. By extensive experiments we verify that the proposed architecture achieves more clearly separated subtypes, as well as medically significant insights into real subtyping.

Sleep screening is not only a major tool in the assessment of pathophysiology, but also a bridge between the central neuronal systems and behaviour/cognition. Automatic sleep staging is an alternative for the time-consuming gold standard manual scoring procedure. Most of the existing works designed such procedure by using deep neural networks without considering the medical criterion of the sleep staging task. We argue that capturing the stage-specific features which meet the criterion is of significant importance for the automatic sleep staging alternative. In this work we propose an end-to-end sleep staging simulator based on mixed neural networks, i.e., CNN, LSTM, and Transformer. The framework consists of two subnetworks: stage dependent feature mapping network which is constructed by the idea of physiological sleep nature, and an attention-based parallel staging network. Moreover, we adopt a mixed precision training strategy to quantize the model for exploring feasible usage in the clinical settings. Through an experiment with a large EEG database (Sleep Heart Health Study), the proposed method has a competitive stage scoring performance, especially in stages Wake, N2, and N3, with higher precision of 0.92, 0.85, and 0.86, respectively. Our study proves that the quantized model has potential capability for further application in the clinical staging task.

The gut microbiota is crucial for human physiology and susceptibility to diseases. Knowing the AMR phenotype canfacilitate the understanding of the impact of antibiotics administration on the gut microbiota. Nowadays, whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is widely used in clinical microbiology to predict the AMR phenotype. To release the limitations of the genomic information and improve the WGS-AST prediction, we propose an integrated multi-omics approach, employing a deep generative neural network (VAE: variational auto-encoder). We evaluate the proposed approach by two machine learning techniques (i.e., K-means for clustering and Random Forest for classification). Our evaluation results show that the integrated multi-omics approach achieves relatively better performance than the conventional WGS-AST. Moreover, the integrated multi-omics approach is able to visually reveal AMR phenotype of the gutmicrobiota via antibacterial spectrum. Our work provides evidence that multi-omics information is useful to enhance the WGS-AST prediction.

Recently, deep learning-based methods have been successfully proposed for electrophysiology signal-based sleep staging with promising results. Most existing methods use convolutional layers and recurrent-based architectures to implement a model structure from feature extraction to sequence signal classification. In this study, we propose a method of segmenting electroencephalogram (EEG) and electrooculogram (EOG) data according to frequency bands and construct a Transformer based automatic sleep classification model on top of it. The results show that the classifications of the stage Wake, N3, and REM outperform the state-of-art works, with the Fl-scores of 0.92, 0.85 and 0.91. Our work is the first attempt to explore the feasibility of a truth-involved Transformer-based model with a large-scale sleep database.

Due to the inherent variability such as inter-users anatomical variability and the inter-systems differences, the design of new EEG-based index and a reliable model for sleep stages classification is still the main topic in sleep science. The unsupervised deep learning framework-variational autoencoder (VAE) which can capture the major characteristics of the input by imposing a Gaussian prior distribution on the latent features is suitable in EEG characterization and reconstruction. Although vanilla VAE and convolutional autoencoder (CAE) have been tried, it has yet been discussed that whether a deep structure or a multi-scale structure is more appropriate. In this paper, we constructed a shallow iVAE model, which will capture the multi-scale features of the spectrogram of EEG by replacing the main structure in encoder and decoder with the inception-like structure. By comparing with the vanilla VAE and the CAE, a more accurate reconstruction and a better classification using the latent features of the iVAE can be confirmed.

In this paper, we propose a novel biclustering approach called BiClusO. Biclustering can be applied to various types of bipartite data such as gene-condition or gene-disease relations. For example, we applied BiClusO to bipartite relations between species and volatile organic compounds (VOCs). VOCs, which are emitted by different species, have huge environmental and ecological impacts. The biosynthesis of VOCs depends on different metabolic pathways which can be used to categorize the species. A previous study related to the KNApSAcK VOC database classified microorganisms based on their VOC profiles, which confirmed the consistency between VOC-based and pathogenicity-based classifications. However, due to limited data, classification of all species in terms of VOC profiles was not performed. In this study, we enriched our database with additional data collected from different online sources and journals. Then, by applying BiClusO to species-VOC relational data, we determined that VOC-based classification is consistent with taxonomy-based classification of the species. We also assessed the diversity of VOC pathways across different kingdoms of species.

A biomarker (short for biological marker) is a medical sign of a disease or condition which indicates a normal or abnormal state of a body. The biomarker is a key factor in the analysis of diseases and also for analyzing inter disease relations. In the previous study, we designed and developed a human biomarker (metabolites and proteins) database and the database is currently available online. This work was supported by the Ministry of Education, Japan and NAIST Big Data Project. We have used our previously developed database and collected 486 human biomarkers and their respective diseases. We determined the similarity among NCBI disease classes based on associated biomarker fingerprints. For this purpose, we collected biomarker PubChem IDs and using them downloaded the SDF files in a batch, then with those molecular description files determined their atom pair fingerprints using ChemmineR package. We constructed a network of biomarkers based on Tanimoto similarity between their fingerprints and applied DPclusO algorithm to find clusters consisting of biomarkers with similar chemical structures. We also conducted hierarchical clustering of the biomarkers. We categorized all the diseases in our data into 18 NCBI disease classes. Combining all information, we finally determined inter disease relations based on structural similarity between biomarkers.

BACKGROUND: Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network. Molecular similarity is a crucial metric in the analysis of qualitative structure-activity relationships. However, it is sometimes difficult for current fingerprint representations to emphasize specific features for the target problems efficiently. It is advantageous to allow the model to select the appropriate features according to data-driven decisions for extracting more useful information, which influences a classification or regression problem substantially. RESULTS: In this study, we applied a neural network architecture for undirected graph representation of molecules. By encoding a molecule as an abstract graph and applying "convolution" on the graph and training the weight of the neural network framework, the neural network can optimize feature selection for the training problem. By incorporating the effects from adjacent atoms recursively, graph convolutional neural networks can extract the features of latent atoms that represent chemical features of a molecule efficiently. In order to investigate alkaloid biosynthesis, we trained the network to distinguish the precursors of 566 alkaloids, which are almost all of the alkaloids whose biosynthesis pathways are known, and showed that the model could predict starting substances with an averaged accuracy of 97.5%. CONCLUSION: We have showed that our model can predict more accurately compared to the random forest and general neural network when the variables and fingerprints are not selected, while the performance is comparable when we carefully select 507 variables from 18000 dimensions of descriptors. The prediction of pathways contributes to understanding of alkaloid synthesis mechanisms and the application of graph based neural network models to similar problems in bioinformatics would therefore be beneficial. We applied our model to evaluate the precursors of biosynthesis of 12000 alkaloids found in various organisms and found power-low-like distribution.

Various medicinal plants are available in Bangladesh and these plants are used as traditional medicines for healing and health maintenance. Unani is one of the traditional medicine systems popular among Bangladeshi people because of its high success rate. Disease phenotype is changing constantly. It is Challenging for researchers to get the right medicinal ingredients, for the right disease, within a reasonable time. So we need to analyze the right plants for the right disease based on the existing formulas and to find out the relationship between plant and disease. The predicted plant-disease relations will help the health researcher or pharmacist for finding new drugs for new diseases. In our datasets, we have 409 plants, which are used as ingredients of 609 Unani formulas. Based on 609 formulas, we enlisted and sorted the relationship between diseases and plants. We assigned 609 Unani formulas to 18 National Center for Biotechnology Information (NCBI) disease classes. We then constructed the network of Unani formulas based on their ingredient similarity and applied DPclusO algorithm to find clusters. Clusters are associated with dominant disease and dominant plants by voting thus we established relations between plants and diseases. We predicted associations between 12 diseases and 151 plants. We validated our prediction based on the global set of Unani formulas and obtained 85.57% accuracy

BACKGROUND: There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD. RESULTS: We downloaded IBD gene expression data from NCBI's Gene Expression Omnibus, performed statistical analysis to determine differentially expressed genes, collected known IBD genes from DisGeNet database, which were used to construct a IBD related PPI network with HIPPIE database. We adapted our graph-based clustering algorithm DPClusO to cluster the disease PPI network. We evaluated the statistical significance of the identified clusters in the context of determining the richness of IBD genes using Fisher's exact test and predicted novel genes related to IBD. We showed 93.8% of our predictions are correct in the context of other databases and published literatures related to IBD. CONCLUSIONS: Finding disease-causing genes is necessary for developing drugs with synergistic effect targeting many genes simultaneously. Here we present an approach to identify novel disease genes and pathways and discuss our approach in the context of IBD. The approach can be generalized to find disease-associated genes for other diseases.

BACKGROUND: The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results. RESULTS: In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively. CONCLUSIONS: The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

BACKGROUND: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes. RESULTS: This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. CONCLUSION: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.

Introduces the concept of hierarchical testability, of data paths for delay faults. A definition of a hierarchically two-pattern testable (HTPT) data path is developed. Also, a design for testability (DFT) method is presented to augment a data path to an HTPT one. The DFT method incorporates a graph-based analysis of an HTPT data path and makes use of some graph algorithms. The proposed method can provide similar advantages to the enhanced scan approach at the cost of much lower hardware overhead.

This paper presents the VHDL design of a prototype test processor, which can be used for functional testing of digital ICs. The design of the test processor supports itself to be controlled by a microcomputer. The processor can generate mixed-mode (pseudo-random followed by deterministic) test vectors and can apply them to circuit under test (CUT). The test processor also receives the output responses of the CUT and compresses them to a signature. The signature is then sent to the computer for comparison. The test processor supports the testing of combinational as well as sequential circuits (with scanpath).

This paper presents the design of an off-chip current sensor for IDDQ testing of CMOS ICs. An operational amplifier is used to maintain a virtual ground so that the insertion of the sensor in series with the circuit under test (CUT) does not degrade the operating voltage across the CUT during testing. A voltage controlled switch is used to bypass the transient current peaks. The current (IDDQ) is directly converted to voltage with a conversion factor of 5 mV//spl mu/A without any amplification. Computer simulation shows the performance of the sensor to detect different bridging faults in c17 benchmark circuit with a 100 kHz test frequency.