Bioinformatics Algorithms for Knowledge Extraction in Biomedical Data

Recent advances in biomedical technologies led to the availability of biomedical data. Many open access repositories oer researchers a large number of heterogeneous data, that have to be properly characterized. Bioinformatics, an interdisciplinary field that integrates biology and computer science, arises for organizing and understanding the information contained in the genomic and biomedical data. This Ph.D. thesis focuses on the study of structures of biological macromolecules, in particular with an emphasis on the analysis of primary (i.e., nucleotide sequence) and secondary (i.e, folding of sequence) ones, by developing ad-hoc algorithms and computational procedures for knowledge extraction. On the one hand, this work deals with the classification analysis applied to the primary structures, by means of supervised machine learning algorithms, jointly with feature selection approaches to search for relevant subset(s) of nucleotides (chapter 2 and 3), as well as to address case-control studies (chapter 4). On the other hand, the work addresses the challenging topic of folding and comparing secondary structures of small and long RNAs, looking for structural motifs shared among them (chapter 5). The adopted approach focuses on the development of ad-hoc algorithms and then on applying them to relevant biomedical problems. The results discussed in this dissertation were achieved during my Ph.D. program at the Department of Computer, Control, and Management Engineering (DIAG) of Sapienza University of Rome, jointly with the Institute for Systems Analysis and Computer Science\A. Ruberti" (IASI) of National Research Council (CNR) of Rome and are consolidated by several journal publications and international conference proceedings, which will be properly referred in the text (and listed at the end) and constitute the research output of this dissertation.