Algorithmic Approaches for Molecular Biology Problems -- Genotype Tagging with Limited Overfitting -- Constraint Programming Models for Transposition Distance Problem -- Comparison of Spectra in Unsequenced Species -- Micro-array Analysis -- BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering -- Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets -- Quantitative Improvements in cDNA Microarray Spot Segmentation -- Machine Learning Methods for Classification -- SOM-PORTRAIT: Identifying Non-coding RNAs Using Self-Organizing Maps -- Automatic Classification of Enzyme Family in Protein Annotation -- Representations for Evolutionary Algorithms Applied to Protein Structure Prediction Problem Using HP Model -- Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques -- Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases -- In Silico Simulation -- Influence of Antigenic Mutations in Time Evolution of the Immune Memory – A Dynamic Modeling -- Short Papers -- FReDD: Supporting Mining Strategies through a Flexible-Receptor Docking Database -- A Wide Antimicrobial Peptides Search Method Using Fuzzy Modeling -- Identification of Proteins from Tuberculin Purified Protein Derivative (PPD) with Potential for TB Diagnosis Using Bioinformatics Analysis -- Mapping HIV-1 Subtype C gp120Epitopes Using a Bioinformatic Approach -- MHC: Peptide Analysis: Implications on the Immunogenicity of Hantaviruses’ N protein -- An Ontology to Integrate Transcriptomics and Interatomics Data Involved in Gene Pathways of Genome Stability.

This book constitutes the refereed proceedings of the 4th Brazilian Symposium on Bioinformatics, BSB 2009, held in Porto Alegre, Brazil, in July 2009 The 12 revised full papers and 6 extended abstracts were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on algorithmic approaches for molecular biology problems; micro-array analysis; machine learning methods for classification; and in silico simulation.