Advances in Bioinformatics and Computational Biology 16th Brazilian Symposium on Bioinformatics, BSB 2023, Curitiba, Brazil, June 13–16, 2023, Proceedings

This book constitutes the proceedings of the 16th Brazilian Symposium on Bioinformatics on Advances in Bioinformatics and Computational Biology, BSB 2023, which took place in Curitiba, Brazil, in June 2023. 

The 11 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 24 submissions. The papers focus on bioinformatics, computational biology, Biological Databases, Biological Networks, Cheminformatics, Evolutionary Genomics, Computational Proteomics, Systems Biology, Drug Design, Genomics, Machine Learning applications in Bioinformatics, Metagenomics, Molecular Docking and Modeling, Molecular Evolution and Phylogenetics, Protein Structure and Modeling, Proteomics, Transcriptomics, Single-Cell Analysis, Workflows in Bioinformatics. 

Block Interchange and Reversal Distance on Unbalanced Genomes.- circTIS: a weighted degree string kernel with support vector machine tool for translation initiation sites prediction in circRNA.- Evaluating the molecular-electronic structure and the antiviral effect of functionalized heparin on graphene oxide through ab initio computer simulations and molecular docking.- Make No Mistake! Why Do Tools Make Incorrect Long Non-Coding Rna Classification?.- Spectrum-Based Statistical Methods for Directed Graphs with Applications in Biological Data.- Feature Selection Investigation in Machine Learning Docking Scoring Functions.- Using natural language processing for context identification in COVID-19 literature.- A framework for inference and selection of cell signaling pathway dynamic models.- Intentional Semantics for Molecular Biology.- transcAnalysis: A Snakemake Pipeline for Differential Expression and Post-Transcriptional Modification Analysis.- Peptide-protein interface classification using convolutional neural networks.- A Power Law Semantic Similarity from Gene Ontology.- Gene Networks Inference by Reinforcement Learning Approach.- Exploring Identifiability in Hybrid Models of Cell Signaling Pathways.