UTD : Computational Biology, Spring 2018

Computational Biology

Course No: BIOL6385.001.18S / BMEN6389.001.18S
Spring 2018
Biology Department, The University of Texas at Dallas

Instructor : Michael Zhang
Director, Center for Systems Biology, The University of Texas at Dallas
Associate instructor: Pradipta Ray
Research Scientist, Center for Systems Biology, The University of Texas at Dallas

Consult this page for class, recitation and exam dates, handouts, and solutions.

A printable version of course policy and syllabus is here. Updates to this document will be made on this website.

Slides will be updated after class. Students can view old versions before updated.

Course Schedule

The last day to drop the course without a "W" grade is Jan 24. The last day to drop a graduate course in any way is Mar 26. See the academic calendar for details.

Lecture	Topic	Handouts / readings	NB
Unit 1: Background and Statistical Inference
Unit 1 Class 1	Introduction	Slides	CF gene discovery original paper, hosted at UNC here Mathematical Writing See R tutorial here, PERL tutorials here, Python tutorials here, and MATLAB tutorial here. A database of open source machine learning tools is at mloss.org, here. The not-so-short introduction to LATEX.
Unit 1 Class 2	Introduction (cont'd):Probability Theory	Class notes
Unit 1 Class 3	Introduction (cont'd) Statistical inference	Slides
Unit 1 Class 4	Bayes Nets I : Modelling and Estimation	Dirichlet Notes EM Paper
Unit 1 Class 5	Bayes Nets II : Bayes net/Inference	Slides Bayesian Inference paper
	Last day to drop course without a "W" grade.
Unit 2: Sequence Alignment
Unit 2 Class 1	Alignment I. Scoring Models	Slides	HW1 out HWK1 HWK1 Solutions
Unit 2 Class 2	Alignment II. Dynamic Programming and Global Alignment	Slides
Unit 2 Class 3	Alignment III. Local alignment and heuristics	Random Path Analysis Square Functional equation handout 1 Square Functional equation handout 2
Unit 2 Class 4	Karlin-Altschul Statistics and Score Significance	Local Alignment Handout Alignment Score Significance
Unit 3: Markovian Models
Unit 3 Class 1	Markov Nets I. Markov Chain	Slides Handout
Unit 3 Class 2	Markov Nets II. HMM:Segmentation	Slides FB Algorithm Derivation	HWK 1 due HWK2 HWK Solutions
Unit 3 Class 3	Markov Nets III. HMM: Viterbi, Forward/Backward	Viterbi Algorithm Handout HMM handout
Unit 3 Class 4	HMM : Markov Nets IV. Baum-Welch algorithm	Pair HMM
Unit 3 Class 4	Markov Nets V. Profile HMM	Profile HMM
	Midterm Exam Review		HW2 due
	Midterm exam (in class)	Midterm Solutions
Unit 3 Class 5	Markov Nets VI. HMM vs CRF	Slides Slides_HMM vs CRF Handout
Unit 4: Comparative Genomics and Evolution
Unit 3 Class 6	Evolutionary models I	Slides
	Spring break, no class
	Spring break, no class
Unit 4 Class1	Evolutationary Models II
Unit 4 Class 2	Phylogenetic Trees I	Slides Handout
Unit 4 Class 3	Phylogenetic Trees II		HWK3
Unit 5: Motif finding
Unit 4 Class 4	Motif finding (Greedy, EM, Gibbs sampling)	Slides
	Last date to withdraw with "W" grade (graduate students)
Unit 5 Class 1	Evaluation of significance of motifs	Slides
Unit 5 Class 2	Discriminant motif finding (DWE/DME) Functional motif finding (Regression, CART, MARS)
Unit 6: Machine Learning
Unit 5 Class 3	SVM and Kernel method	ML Introduction SVM SVM_2017 CommentsSVM
Unit 5 Class 4	Ensemble learning, Boosting (Random Forest)	Slides Slides_2017
Unit 5 Class 4	Lasso, Sparsity, Regularization	Ridge_regression Lasso Handout	Notes
Unit 6 Class 1	Deep Learning tutorial	Slides
Unit 6 Class 2	Deep Learning In Computational Biology	DeepBind DeepSea DeepVariant DeepLearning_ISL	HW3 due HWK3_solution
Unit 6 Class 3	Final Exam review
	Final exam in class

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