CMBSB 2015 - Computational Methods in Bioinformatics and Systems Biology

The success of bioinformatics in recent years has been prompted by research in molecular biology and medicine in initiatives like the human genome project. These initiatives gave rise to an enormous increase in the volume and diversification of data, including protein and genomic sequences, high-throughput experimental and biomedical literature. The widespread availability of low-cost, full genome sequencing, will introduce new challenges to bioinformatics as a key field to enable personalized medicine. New methods are needed to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.
Systems Biology is a related field, devoted mainly to efforts in cell modeling, that requires the coordinated efforts of biological researchers with those related to data analysis, mathematical model-ling, computer simulation and optimization.
The accumulation and exploitation of large-scale databases prompts for new computational technologies 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 experimental data, are based on Artificial Intelligence (AI) techniques.
In fact, these methods have been helping in tasks related to knowledge discovery, model-ling and optimization tasks, aiming at the development of computational models so that the response of biological complex systems to any perturbation can be predicted. Hence, this workshop brings the opportunity to discuss applications of AI with an interdisciplinary character, exploring the interactions between sub-areas of AI, Bioinformatics and Systems Biology.
TOPICS OF INTEREST
Biological areas of interest include, but are not limited to:
Sequence analysis, comparison and alignment methods;
Motif, gene and signal recognition;
Molecular evolution, phylogenetics and phylogenomics;
Determination or prediction of the structure of RNA and protein in two and three dimensions;
Inference/ reconstruction of metabolic/ regulatory networks or models;
Analysis of high-throughput biological data (trancriptomics, proteomics, metabolomics, fluxomics);
Functional genomics;
Molecular docking and drug design;
Problems in population genetics such as linkage and QTL analysis, linkage disequilibrium analysis in populations, and haplotype determination;
Metabolic engineering applications.
Computational areas of interest include, but are 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;
Non-linear dynamical analysis methods and Intelligent signal processing.