Computational Advances in Bio and Medical Sciences 12th International Conference, ICCABS 2023, Norman, OK, USA, December 11–13, 2023, Revised Selected Papers

This book constitutes the refereed proceedings of the 12th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2023, held in Norman, Oklahoma, USA, during December 11–13, 2023.

The 23 full papers included in this book were carefully reviewed and selected from 65 submissions. These papers focus on the recent advances in Computational techniques and applications in the areas of Biology, Medicine, and Drug discovery.

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.