CS598 Machine Learning in Computational Biology (Lecture 1: Introduction) Professor Jian Peng Teaching Assistant: Rongda Zhu

Transcription

1 CS598 Machine Learning in Computational Biology (Lecture 1: Introduction) Professor Jian Peng Teaching Assistant: Rongda Zhu

2 Introduction Instructor: Jian Peng My office location: 2118 SC Office hour: Thursday, 3:15pm-4:45pm My own research: Computational Biology and Graphical Models Teaching Assistant: Rongda Zhu, PhD student (Department of Computer Science) Rongda s research: Machine Learning and Probabilistic Inference Course website: teaching/cs598_fall15/index.htm

3 Course Information Schedule (tentative) Introductory lectures (Aug 25 to Sep 8) Biology data analysis Probabilisitic models Student presentations (Sep 8 to Dec 3) Research survey Research article Course projects Proposal presentation (Oct 6 & 8) Final presentation (Dec 8 &10)

4 Objectives Introduction to computational biology Important problems in computational biology Machine learning techniques for data analysis Understand how methods work Learning to do research Paper presentation Ability to present key ideas to other people Ability to ask critical questions Course project experience Hands-on practice with real datasets Propose and perform independent research Active participation in the field

5 Prerequisites Biology: Basic concepts in molecular biology Reference: Molecular Biology for Computer Scientists by Lawrence Hunter Machine Learning: Probability and statistics Optimization Textbook: Pattern Recognition and Machine Learning by Christopher Bishop

6 Grading Class attendance: 10% Presentation: 30% Course Project: 60% Proposal Report Presentation

7 Presentation Discuss papers you would like to present with me at least one week before your presentation Research survey (at least five papers) Methodology: applications to different problems Research problem: the state-of-the-art methods Research article (preferred) Background: what is the problem? why important? Methodology: how does it work? Results: what are the findings? any conclusions? Open-ended Q & A and debate

8 Questions about the presentation?

9 Course Project Computational techniques Novel machine learning algorithms Efficient algorithms that scale on large datasets New probabilistic models for biological data Biological problems New biological findings Improvements over existing method New computational biological problems The goal is to have something publishable or presentable in conferences or journals.

10 Course Project Proposal presentation (Oct 6 & 8) written proposal due by Oct 4 at least four pages discuss with me about your projects in Sep 15-min presentation in class I will also give you a list of potential projects if you don t have one by Sep 20. Final presentation (Dec 8 &10) Report due by Dec 12 at least eight pages 15-min oral presentation and poster

11 Course Project Team size one or two make clear your contribution in the project report Implementation put your code/data on github get your hands dirty and work on real-world datasets your contribution should be original

12 Questions about the course project?

13 Introduce yourself

14 Why computational biology is hard? High-dimensional Noisy Huge Sparse

15 Biological Data Sequence data Protein/DNA sequence Generative and discriminative models for sequences Deep learning Matrix data Gene expression Dimensionality reduction and feature selection Low-rank approximation

16 Biological Data Network data Molecular network Random walk algorithms Graphical models and approximate inference Heterogeneous data Dimensionality reduction Probabilistic models for data integration Network-based data integration

17 Machine Learning Supervised learning Prediction: classification: SVM, logistic regression, random forest structured output: CRF, structured SVM Feature finding: Sparse learning: LASSO and elastic nets Unsupervised learning Dimensionality reduction and embedding: manifold learning: Isomap, LLE, t-sne component analysis: PCA, ICA Probabilistic modeling: graphical model: HMM, Bayesian networks, RBM methodology: variational inference, sampling

18 TODO after this class Please read Molecular Biology for Computer Scientists by Lawrence Hunter

19 Examples of my research projects

20 Protein sequence, structure and function sequence ACDEEEFGHIKL----MPQRSTVWY ACDE--FGHIKLRMQP----STVWY structure function

21 Network analysis for disease modeling network analysis new disease biology (potential drug targets) human disease network

22 Pharmacogenomics and cancer genomics Figure from the DREAM challenge website

23 Integration of heterogeneous data

24 Search engine for drug discovery Cell type Pathway Drug on/off interaction Protein membership association perturbation Side effect association Disease association Mutation association

25 Network embedding Diffusion Component Analysis

26 Variational inference

27 Approximate inference Discriminance sampling for partition function estimation Sampling Classification Restricted Boltzmann Machine Deep Boltzmann Machine Combining variational inference and sampling approaches