Universitŕ degli Studi di Milano
Molecular Biotechnologies and Bioinformatics
The lectures deal with computational methods and techniques for Computational Biology and Bioinformatics, covering both programming languages for Bioinformatics and Machine Learning Methods for Computational Biology.
At the end of the course the student should acquire:
1. The R programming language.
2. Machine Learning and Computational Biology.
Lectures and lab exercises, where each student will have a personal computer at his/her disposal.
Development of a Bioinformatics R software project.
The data needed for the project are downloadable from here.
Project groups with the corresponding data to be analyzed.
When and where
Lessons begin: 8 November 2016 and end: January 2017
Aula informatica Via Celoria 20, Milano
Anacleto Lab (Computational Biology and Bioinformatics at the Computer Science Dept., University of Milan)
R Slides (in italian):
Link to the directory with the Machine Learning and Computational Biology slides
Papers (to be updated)
- P. Larranaga et al. Machine learning in bioinformatics, Briefings in Bioinformatics 7(1):86-112, 2006
- Y. Jiang et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy, Genome Biology, 17:184 September 2016.
- Barabasi A, Gulbahce N, Loscalzo J. Network medicine: a network-based
approach to human disease. Nature Rev Genet 12:56–68.2011.
- X. Z. Zhou, J. Menche, A.-L. Barabási, 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, Nature 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.
- 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