Critical challenges for Unmanned Aerial Vehicle (UAV) networks in FANETs operation include collision avoidance, energy efficiency, and decision transparency in highly dynamic aerial conditions. This paper proposes an Explainable Adaptive Path Reconfiguration framework by integrating Transformer-based trajectory prediction, Secretary Bird Optimization Algorithm (SBOA), and SHAP-driven interpretability, enabling predictive, self-aware UAV navigation. The Transformer model predicts future UAV and obstacle trajectories under multi-head self-attention to proactively reconfigure before any potential collision risk. Then, the SBOA further optimizes the trajectories by minimizing a multi-objective cost function that jointly considers collision risk, energy consumption, and path deviation. It estimates the quantitative contribution of key features distance, velocity, and orientation-to each decision made, guaranteeing human-interpretable autonomy. Extensive simulations for diverse UAV densities and obstacle environments prove that EAPR achieves 96.2% collision avoidance, 82.8% energy efficiency, and 0.128 path deviation with improvements of 15%~25% over PSO, FHOA, and PSOA. Robustness tests further confirm resilience in case of communication loss and dense obstacle scenarios. This paper establishes a unified paradigm of predictive, optimized, and explainable UAV swarm navigation for safe and transparent autonomous operations in next-generation aerial networks.

Precision medicine tailors medical procedures to individual genetic overviews and offers transformative solutions for rare genetic conditions. Machine learning (ML) has enhanced genome-based precision medicine (GBPM) by enabling accurate diagnoses, customized treatments, and risk assessments. ML tools, including deep learning and ensemble methods, process high-dimensional genomic data and reveal discoveries in rare diseases. This review analyzes the ML applications in GBPM, emphasizing its role in disease classification, therapeutic optimization, and biomarker discovery. Key challenges, such as computational complexity, data scarcity, and ethical concerns, are discussed alongside advancements such as hybrid ML models and real-time genomic analysis. Security issues, including data breaches and ethical challenges, are addressed. This review identifies future directions, emphasizing the need for comprehensible ML models, increasing data-sharing frameworks, and global collaborations. By integrating the current research, this study provides a comprehensive perspective on the use of ML for rare genetic disorders, paving the way for transformative advancements in precision medicine.

This research addresses the pressing challenge of weed identification in agriculture, crucial for ensuring food security in anticipation of a global population exceeding 9.7 billion by 2050. Utilizing drone imagery, we collected a dataset and proposed a customized model to achieve optimal performance. Our proposed model uses strategically modified backbone, neck, and head components, leveraging elements such as Ghost Convolution, BottleNeckCSP, and ECA (Efficient Channel Attention) layers. These modifications enhance the model’s capability to discern intricate patterns in drone imagery, ultimately leading to improved precision in weed detection. We introduce a purposefully crafted dataset to complement the model’s training, and our experiments demonstrate superior performance compared to the baseline models. Our model achieves a precision of 72.5%, recall of 68.0%, and [email protected] of 73.9, showcasing the effectiveness of our approach in enhancing detection accuracy. Leveraging a unique blend of feature extraction mechanisms, our model achieves remarkable accuracy in real-time soybean detection, outperforming established models like RT-DETR (Real-Time DEtection TransfoRmer) and YOLOv10. A detailed ablation study and comparative analysis with different YOLO versions and the transformer-based RT-DETR showcase the effectiveness of the proposed enhancements. Our work signifies a significant step towards advancing the field of precision agriculture, offering a model that is not only adaptive but also robust in identifying and localizing weeds in soybean fields.

MOTIVATION: The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states. RESULTS: In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses. AVAILABILITY AND IMPLEMENTATION: The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.

N6-methyladenine (6mA), is amongst the most prevalent post-transcriptional epigenetic changes and is crucial for several cellular functions and disease development. Therefore, a thorough knowledge of cellular processes and other potential functional pathways depends on the proper identification of 6mA modifications. Although we have a number of experimental methods for identifying 6mA-modification sites, computational prediction has emerged as a substitute method as a result of the expensive and labor-intensive nature of experimental methods. Considering this, it is of utmost importance to develop a reliable and effective approach for N6-methy1adenine identification. To categorize genome-wide 6mA locations, numerous computational models have previously been proposed, but there is still potential for advancement in their ability to anticipate 6mA sites. Therefore, we developed a technique using Transformers and neural networks for the accurate prediction of 6mA modification sites in Homo sapiens and Mus musculus genomes and obtained high accuracy of 96.5% and 96.86%, respectively using 5-fold cross-validation technique, and 93.75% on Homo sapiens independent test set. The outcomes demonstrate that the presented model outperforms current approaches in terms of all assessment metrics.

An important post-transcriptional alteration is 5-methylcytosine (m5C). It has become more prevalent as technology has advanced in many different RNAs. Previous research has shown that m5C is crucial for many biological processes, including tRNA recognition, RNA stability and the metabolic process of RNA. Traditional detection processes are expensive and time-consuming. As a result, computer models are frequently employed to locate the m5C locations. It is possible to create an effective prediction model because of deep learning’s extensive computing capabilities. Therefore, developing a reliable computational method for predicting RNA modification sites is essential. In this work, we have proposed a CapsuleNet-based architecture for the classification of m5C sites. The one-hot technique is used to convert biological sequences into numerical data. The CNN model is then given the numerical data in order to extract the concealed information. Further, this concealed information is shared with the CapsuleNet framework to get the m5C sites classified. The proposed architecture is evaluated on the human m5C dataset. CapsuleNet-based architecture has demonstrated improved results when compared with already available predictors.

Epigenetic modifications have a vital role in gene expression and are linked to cellular processes such as differentiation, development, and tumorigenesis. Thus, the availability of reliable and accurate methods for identifying and defining these changes facilitates greater insights into the regulatory mechanisms that rely on epigenetic modifications. The current experimental methods provide a genome-wide identification of epigenetic modifications; however, they are expensive and time-consuming. To date, several machine learning methods have been proposed for identifying modifications such as DNA N6-Methyladenine (6mA), RNA N6-Methyladenosine (m6A), DNA N4-methylcytosine (4mC), and RNA pseudouridine ( Ψ). However, these methods are task-specific computational tools and require different encoding representations of DNA/RNA sequences. In this study, we propose a unified deep learning model, called ZayyuNet, for the identification of various epigenetic modifications. The proposed model is based on an architecture called, SpinalNet, inspired by the human somatosensory system that can efficiently receive large inputs and achieve better performance. The proposed model has been evaluated on various epigenetic modifications such as 6mA, m6A, 4mC, and Ψ and the results achieved outperform current state-of-the-art models. A user-friendly web server has been built and made freely available at http://nsclbio.jbnu.ac.kr/tools/ZayyuNet/.