Computational intelligent methods for the analysis of molecular data

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Computational Molecular Biology has emerged as a strategic discipline at the frontier between biology and computer science, and has impacts in medicin, biotechnology and society in many ways. The current research drive in the fields of bioinformatics is to be able to understand evolutionary relationships in terms of the expression of protein function. However, conventional computer science tools are being increasingly unable to address many of the most interesting sequence analysis problems. This is due to the large sizes of the molecular data sets as well as the structural and functional complexities of the genome. My research aims at developing machine learning algorithms that will extend the range of existing applications in bioinformatics. Issues to be addressed are as follows. Current machine learning (as well as feature extraction and selection) approaches to DNA and protein information (such as sequence, structure or function) analysis will be parallelized onto parallel, distributed or grids computing architecture to allow fast analysis. Intelligent methods for DNA and protein information identification, classification, prediction, mining and recognition will be studied in order to build more efficient analysis systems. Biologically inspired heuristics will be applied for the solutions of selected computationally hard bioinformatics problems. Finally, new sequence information retrieval methods that combine multiple DNA and protein information will be investigated. Applications will be conducted on different bioinformatics problems domains.