Universitą degli Studi di MilanoCorso di laurea magistrale in Biotecnologie Molecolari e
Bioinformatica
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| Il corso ha come obiettivo fornire strumenti metodologici per l'analisi di dati biomolecolari complessi, tramite lo studio e l'applicazione di metodi di apprendimento automatico. Il corso tratta alcuni problemi rilevanti nell'ambito della bioinformatica, inserendoli nel contesto delle principali aree di ricerca di questa disciplina. Le lezioni alternano una trattazione intuitiva dei metodi di biologia computazionale con laboratori in cui le nozioni apprese sono applicate all'analisi di dati biomolecolari reali. Prerequisiti: Sono richieste conoscenze di base sul linguaggio R. All'inizio del corso verrą comunque svolto un modulo didattico dedicato a tale linguaggio. Per riferimenti a testi ed a materiale didattico sul linguaggio R, gli studenti possono fare riferimento alla pagina web del corso di Informatica Avanzata. Programma del corso:
Bibliografia: - G. Yona, Introduction to Computational Proteomics, Chapman & Hall/CRC, 2011. Chapter 1: What is Computational Proteomics?; Chapter 7: Classifiers and kernels; Chapter 10 Clustering and Classification; Chapter 12 Analysis of Gene ExpressionData; Chapter 13 Protein-protein interactions; Chapter 14 Cellular Pathways. - Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer 2005: Chapter 1 preprocessing overview; Ch.2 Preprocessing high-density Oligonucleoide arrays; Ch.3 Quality assessment of Affymetrix GeneChip data; Ch. 10 Visualizing data; Ch.14 Analysis of Differential Expression Studies. Articoli: - P. Larranaga et al. Machine learning in bioinformatics, Briefings in Bioinformatics 7(1):86-112, 2006 - A.Bertoni, G.Valentini, Model order selection for biomolecular data clustering, BMC Bioinformatics, vol.8, Suppl.3, 2007. - Barabasi A, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nature Rev Genetics 12:56–68.2011. - X. Z. Zhou, J. Menche, A.-L. Barabįsi, A. Sharma Human symptoms–disease network Nature Communications 5:4212, 1-10 (2014) - J. Menche, A. Sharma, M. Kitsak, D. Ghiassian, M. Vidal, J. Loscazlo, A.-L. Barabasi Uncovering disease-disease relationships through the incomplete interactome Science 347:6224, 1257601-1, 2015. - Y. Moreau, L. Tranchevent Computational tools for prioritizing candidate genes: boosting disease gene discovery, Nat Rev Genet, 13 (8), pp. 523-536, 2012. - S. Aerts, D. Lambrechts, S. Maity, P. Van Loo, B. Coessens, F. De Smet, et al. Gene prioritization through genomic data fusion Nature Biotechnology, 24 (5) 2006. - M. Kann Protein interactions and disease: computational approaches to uncover the etiology of diseases, Brief Bioinform, 8(5), 2007. - R. Sharan, I. Ulitsky and R. Shamir, Network-based prediction of protein function , Molecular Systems Biology 3:88, 2007. - S. Kohler, S. Bauer, D. Horn and P. Robinson, Walking the Interactome for Prioritization of Candidate Disease Genes, Am J Hum Genet. 82(4): 949–958 , 2008. - S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios and Q. Morris, GeneMANIA: A Real-Time Multiple Association Network Integration Algorithm for Predicting Gene Function, Genome Biology, vol. 9, article S4, 2008. - H. Chua, W. Sung and L. Wong, An Efficient Strategy for Extensive Integration of Diverse Biological Data for Protein Function Prediction, Bioinformatics, vol. 23, no. 24, pp. 3364-3373, 2007. - A. Mitrofanova, V. Pavlovic and B. Mishra, Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data, IEEE/ACM Trans Computational Biology and Bioinformatics, vol. 8, no. 3, pp. 775-784, May/June 2011. - M. Re, M. Mesiti and G. Valentini, A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks, IEEE ACM Transactions on Computational Biology and Bioinformatics 9(6) pp. 1812-1818, 2012. IEEE link - M. Mesiti, M. Re and G. Valentini, A Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction , GigaScience, 3 (2014), p. 5 doi: 10.1186/2047-217X-3-5 gigascience link - G. Valentini, A. Paccanaro, H. Caniza, A. Romero, M. Re, An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods, Artificial Intelligence in Medicine, 61:2, pp.63-78, June 2014 - T. Sevimoglu, K. Y. Arga, The role of protein interaction networks in systems biomedicine, Computational and Structural Biotechnology Journal, Volume 11, Issue 18, pp. 22-27, August 2014 Libri di riferimento:
Slide delle lezioni
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