Computational Advances in Bio and Medical Sciences

An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography Volume.- Identification of Chimeric RNAs: A Novel Machine Learning Perspective.- PartialFibers: An Efficient Method for Predicting Drug-Drug Interactions.- Optimizing Deep Learning for Biomedical Imaging.- Exploring a Solution Curve in the Phase Plane for Extreme Firing Rates in the Izhikevich Model.- Cancer and Tissue Prediction Using Mutational Signatures in Highly Mutated Cancers.- On the Hardness of Wildcard Pattern Matching on de Bruijn Graphs.- Plastic: An Easy to use and Modular Tool for Designing Tumor Phylogeny Reconstruction Pipelines.- A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans.- A Simple and Interpretable Deep Learning Model for Diagnosing Pneumonia from Chest X-Ray Images.- FedDP: Secure Federated Learning with Differential Privacy for Disease Prediction.- Computational Tumor Progression Analysis via Seriation based Trajectory Inference.- Multilayer Network Analysis of Brain Signals for Detecting Alzheimer’s Disease.- DNA Methylation Based Subtype Classification of Breast Cancer.- Repeated Measures Latent Dirichlet Allocation for Longitudinal Microbiome Analysis.- Improving Disease Comorbidity Prediction with Biologically Supervised Graph Embedding.- Lightweight and Generalizable Model for COVID-19 Detection Using Chest Xray Images.- Decoding Heterogeneity in Quadruple-Negative Breast Cancer: A Data-Driven Clustering Approach.- Determining Temporal Linkages in Dynamic Epidemiological Networks Using the Earth Mover’s Distance.- Functional Connectivity Disruptions in Alzheimer’s Disease: A Maximum Flow Perspective.- On Multi-Phase Metagenomics Reads Binning.- A Unified Machine Learning Framework for Multi-subtype Tumour Classification across Diverse Datasets.- AFA: Abstract Functional Analysis Identifies New Microglial Subtypes at Single Cell Level in Alzheimer’s Disease.