The success of Bioinformatics in recent years has been prompted by research in Molecular Biology and Molecular Medicine in several initiatives. These initiatives gave rise to an exponential increase in the volume and diversification of data, including nucleotide and protein sequences and annotations, high-throughput experimental data, biomedical literature, among many others.

Systems Biology is a related research area that has been replacing the reductionist view that dominated Biology research in the last decades, requiring the coordinated efforts of biological researchers with those related to data analysis, mathematical modeling, computer simulation and optimization.

The accumulation and exploitation of large-scale data bases prompts for new computational technology and for research into these issues. In this context, many widely successful computational models and tools used by biologists in these initiatives, such as clustering and classification methods for gene expression data, are based on Computer Science/ Artificial Intelligence (CS/AI) techniques. In fact, these methods have been helping in tasks related to knowledge discovery, modeling and optimization tasks, aiming at the development of computational models so that the response of biological complex systems to any perturbation can be predicted.

The 12th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB)
aims to promote the interaction among the scientific community to discuss applications of CS/AI with an interdisciplinary character, exploring the interactions between sub-areas of CS/AI, Bioinformatics, Chemoinformatics and Systems Biology.

Brand new ideas in these fields are sought, as well as substantial and relevant revisions and actualizations of previously presented work, project summaries and PhD thesis presented or not.

PACBB welcomes contributions reporting substantial, original and previously unpublished work in all areas of Bioinformatics, Chemoinformatics and Systems Biology. The papers can be presented from a formal, methodological, technical or applied point of view.

Biological areas of interest include, but are not limited to:

    Next generation sequencing
    Comparison and alignment methods
    Motif, gene and signal recognition
    Molecular evolution
    DNA twisting and folding
    High-throughput data analysis (transcriptomics, proteomics, etc)
    Phylogenetics and phylogenomics
    Determination or prediction of the structure of RNA and proteins
    Gene expression data analysis
    Identification of metabolic pathways
    Functional genomics
    Biomarker identification
    Molecular docking and drug design
    Computational problems in genetics such as linkage and QTL analysis, linkage disequilibrium analysis in populations, haplotype determination, SNPs
    Molecular and cellular interactions
    Emergence of properties in complex biological systems
    Visualization of biological systems and networks
    Data and software integration
    Biomedical ontologies
    In silico optimization of biological systems
    Metabolic engineering applications
    Cell simulation and modeling
    Metabolic, regulatory and signaling models/ networks: properties, dynamics, inference/ reconstruction
    Metabolomics/ metabolic fingerprints
    Health-care applications
    Bio imaging

Computacional areas of interest include, but ar not limited to:

    Knowledge Discovery and Data Mining techniques
    Text Mining and Language Processing
    Machine Learning and Pattern Recognition
    Rough, Fuzzy and Hybrid Techniques
    Hidden Markov Models
    Bayesian Approaches
    Artificial Neural Networks
    Support Vector Machines
    Evolutionary Computing
    Case-Based Reasoning systems
    Non-linear dynamical analysis methods and Intelligent signal processing
    Feature selection