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.  

Table of Contents:
.- 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.