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Computational advances in bio and medical sciences = 12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023 : revised selected papers /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational advances in bio and medical sciences/ edited by Mukul S. Bansal ... [et al.].
其他題名:	12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023 : revised selected papers /
其他題名:	ICCABS 2023
其他作者:	Bansal, Mukul S.
團體作者:	Workshop on the Preservation of Stability under Discretization
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xiii, 278 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-031-82768-6
ISBN:	9783031827686

Computational advances in bio and medical sciences = 12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023 : revised selected papers /
Computational advances in bio and medical sciences12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023 : revised selected papers /[electronic resource] :ICCABS 2023edited by Mukul S. Bansal ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xiii, 278 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,145481611-3349 ;. - Lecture notes in bioinformatics..

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.

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.

ISBN: 9783031827686

Standard No.: 10.1007/978-3-031-82768-6doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QH324.2

Dewey Class. No.: 570.285

Computational advances in bio and medical sciences = 12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023 : revised selected papers /
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