Computational Intelligence Methods for Bioinformatics and Biostatistics

18th International Meeting, CIBB 2023, Padova, Italy, September 6-8, 2023, Revised Selected Papers
Herausgegeben:Vettoretti, Martina; Tavazzi, Erica; Longato, Enrico; Baruzzo, Giacomo; Bellato, Massimo

• Broschiertes Buch

Produktbeschreibung

The book constitutes the refereed post-conference proceedings of the 18th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2023, held in Padova, Italy, during September 6 8, 2023.

The 23 full papers presented in these proceedings were carefully reviewed and selected from 24 submissions. They focuses on topics such as machine learning in healthcare informatics and medical biology; machine learning explainability in medical imaging; prediction uncertainty in machine learning; advanced statistical and computational methodologies for single-cell omics data; present and future research in bioinformatics; distributed computing in bioinformatics and computational biology; and modelling and simulation methods for computational biology and systems medicine.

Produktdetails

• Lecture Notes in Computer Science 14513
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-90713-5
• Seitenzahl: 352
• Erscheinungstermin: 13. Mai 2025
• Englisch
• Abmessung: 235mm x 155mm x 20mm
• Gewicht: 534g
• ISBN-13: 9783031907135
• ISBN-10: 3031907132
• Artikelnr.: 73687998

Inhaltsangabe

.- A Network Approach to Aquatic Food Web Dynamics.

.- Leveraging Diffuser Data Augmentation to enhance ViT-based performance on Dermatoscopic Melanoma Images Classification.

.- Thyroid Nodule Diagnosis Using a New Supervised Autoencoder Neural net work with multi-categorical medical data.

.- Can smoothing methods recognize the patterns of the hazard function in complex clinical scenarios? A simulation study using discrete-time survival models.

.- Nested Named Entity Recognition in Chinese Electronic Medical Records.

.- Transformers for Interpretable Classification of Histopathological Images.

.- Breast Cancer Malignancy Prediction Through Explainable Models based on a Multimodal Signature of Features.

.- Exploring the Conformational Odorant Space in the Olfactory Re-ceptor Binding Region.

.- Synergy between mechanistic modelling and Ensemble Feature Selection ap proaches to explore multiscale biological Heterogeneity.

.- Homophily of large weighted networks in a data streaming setting.

.- Living along COVID-19: assessing contention policies through Agent-Based Models.

.- Stochastic modeling and dosage optimization of a cancer vaccine exploiting the EpiMod Framework.

.- Extension of the GreatMod modeling framework to simulate non-Markovian processes with general-distributed events.

.- Identifying Damage-Related Features in scRNA-seq Data.

.- A benchmark study of gene fusion prioritization tools.

.- Improving the reliability of tree-based feature importance via consensus signals.

.- Interpretable Machine Learning for Automated Cellular Population Analysis in Flow Cytometry.

.- Pre-trained Models Based on Primary Sequence to Classify Antibody Bind ing to Protein and Non-Protein Targets with 80% Accuracy.

.- Inferring breast cancer subtype associations using an original omics integra tion based on Non-negative Matrix Tri-Factorization.

.- Screening the bioactivity of the P450 enzyme by spiking neural networks.

.- Enhancing functional interpretability in gene expression analysis through biologically-guided feature selection.

.- Extraction of Attributes from Electrodermal Activity Signals Applying Time Series Fuzzy Granulation for Classification of Academic Stress Perception in Different Scenarios.

.- Transfer Learning and AutoML as a Support for the Pneumonia Diagnosis using Chest X-ray scan.