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Inference in biological systems

01TYLPF

A.A. 2019/20

2019/20

Inference in biological systems

The course provides an introduction to molecular biology and to quantitative methods that can be used to extract information from complex biological systems, including the analysis of DNA, RNA and protein sequences, the reconstruction of phylogenetic trees, and the use of machine-learning techniques to analyze the structure of gene and protein networks.

Inference in biological systems

The course provides an introduction to molecular biology and to quantitative methods that can be used to extract information from complex biological systems, including the analysis of DNA, RNA and protein sequences, the reconstruction of phylogenetic trees, and the use of machine-learning techniques to analyze the structure of gene and protein networks.

Inference in biological systems

• understanding basics notions of molecular biology; • understanding basic approaches to sequence alignment; • being familiar with structural inference and maximum entropy techniques; • being able to code simple sequence-alignment algorithms; • being able to apply basic machine-learning methods to given genetic and biological problems.

Inference in biological systems

• understanding basics notions of molecular biology; • understanding basic approaches to sequence alignment; • being familiar with structural inference and maximum entropy techniques; • being able to code simple sequence-alignment algorithms; • being able to apply basic machine-learning methods to given genetic and biological problems.

Inference in biological systems

Basics of probability theory, principles of statistical physics, basic programming skills (any language).

Inference in biological systems

Basics of probability theory, principles of statistical physics, basic programming skills (any language).

Inference in biological systems

• Introduction to Molecular Biology: central dogma, DNA, RNA, proteins; gene regulation; metabolism; (20 h, C. Bosia) • Hidden Markov models: from pairwise to multiple sequence alignments; inference in protein families; phylogeny reconstruction; RNA folding; (20 h, A. Gamba) • Machine learning techniques: introduction to widely used methodologies (e.g. neural networks, random forests, convolution neural networks, Bayesian neural networks, recurrent neural networks, LSTM); application to genetic and biological studies; (20 h, E. Ficarra).

Inference in biological systems

• Introduction to Molecular Biology: central dogma, DNA, RNA, proteins; gene regulation; metabolism; (20 h, C. Bosia) • Hidden Markov models: from pairwise to multiple sequence alignments; inference in protein families; phylogeny reconstruction; RNA folding; (20 h, A. Gamba) • Machine learning techniques: introduction to widely used methodologies (e.g. neural networks, random forests, convolution neural networks, Bayesian neural networks, recurrent neural networks, LSTM); application to genetic and biological studies; (20 h, E. Ficarra).

Inference in biological systems

Inference in biological systems

Inference in biological systems

The course alternates lectures on theoretical topics (approximately 45 hours) and hands-on computer lab (approximately 15 hours), where the students will be asked to apply theoretical ideas and algorithms to selected problems.

Inference in biological systems

The course alternates lectures on theoretical topics (approximately 45 hours) and hands-on computer lab (approximately 15 hours), where the students will be asked to apply theoretical ideas and algorithms to selected problems.

Inference in biological systems

• Course handouts • B. Alberts et al., Molecular Biology of the Cell, Garland Science, 2015 • R. Durbin et al., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge Un. Press, 2002 • H.C. Nguyen, R. Zecchina and J. Berg, Inverse statistical problems: from the inverse Ising problem to data science, Adv. Phys., 66 (2017) 197-261. • S. Cocco et al., Inverse statistical physics of protein sequences: a key issues review, Rep. Progr. Phys. 81 (2018) 032601. • J. Felsenstein, Inferring phylogenies, Sinauer Associates, 2004 • C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011 • Suggested scientific publications

Inference in biological systems

• Course handouts • B. Alberts et al., Molecular Biology of the Cell, Garland Science, 2015 • R. Durbin et al., Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge Un. Press, 2002 • H.C. Nguyen, R. Zecchina and J. Berg, Inverse statistical problems: from the inverse Ising problem to data science, Adv. Phys., 66 (2017) 197-261. • S. Cocco et al., Inverse statistical physics of protein sequences: a key issues review, Rep. Progr. Phys. 81 (2018) 032601. • J. Felsenstein, Inferring phylogenies, Sinauer Associates, 2004 • C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011 • Suggested scientific publications

Inference in biological systems

Modalitΰ di esame: Prova orale obbligatoria; Progetto individuale;

Inference in biological systems

Inference in biological systems

Exam: Compulsory oral exam; Individual project;

Inference in biological systems

The oral test will consist of 2-3 broad questions on the main topics of the lectures. The individual project will consist of the application of computational and machine-learning techniques to a given biological problem, and will be presented as ppt slides in an oral discussion. The grading of the project will contribute to 1/3 of the overall assessment.

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