CMSC498Y meets on Mondays and Wednesdays.
The course schedule will be updated here, along with links to materials and assignments.
Slides will be posted to ELMS-Canvas before the lecture and then linked after the lecture.
If lecture is recorded, the video will typically be available under the zoom tab on ELMS.
The overall schedule will be similar to last year (spring 2025), with some improvements to pacing and content.
Readings come from journal articles or one of three textbooks:
- BSA - Biological Sequence Analysis textbook by Durbin, Eddy, Krogh, and Mitchison [UMD Library]
- DL - Deep Learning by Ian Goodfellow, Yuoshua Bengio, and Aaron Courville
- ISB - Introduction to Structural Bioinformatics edited by K. Anton Feenstra and Sanne Abeln
| Week | Day | Date | Module | Topic | Materials | Assigned Reading |
|---|---|---|---|---|---|---|
| 1 | Mon | Jan 26 | No class | Inclement weather | ||
| Wed | Jan 28 | Welcome! | Course Overview, Policies, and Basic Biology Review [slides] | None | ||
| 2 | Mon | Feb 2 | A | Random Sequence Model |
Random Sequence Model and Statistics Review (including maximum likelihood parameter estimation)
[slides]
Assignment #1 released. |
BSA Sections 1.3, 11.1 (through multinomial), 11.2 (only relative entropy), 11.3 (only ML), 11.5 (only ML) |
| Wed | Feb 4 | A | Markov Models | Binary Classification, Bayes Classifier, Classifier Evaluation (precision, recall, etc), Markov Models, Hidden Markov Models (definition only) [slides] | BSA Sections 3.1, 3.2 (through formal definition of HMM) | |
| 3 | Mon | Feb 9 | A | Decoding HMMs | Viterbi algorithm, Posterior decoding (Forward + Backward algorithm), Recursion vs. Dynamic Programming [slides] | BSA Section 3.2 |
| Wed | Feb 11 | B | MSAs |
Multiple Sequence Alignment (MSA), Sum-of-Pairs (SOP) error, Hamming and edit distance, Sum-of-Pairs (SOP) alignment, Star alignment heuristic
[slides]
Also, add /drop period ends in two days (Fri Feb 13) |
None | |
| 4 | Mon | Feb 16 | B | Profile HMMs |
Unadjusted Sequence Profiles, Profile HMMs, Supervised Training given MSA, Pseudocounts, Decoding with Viterbi Algorithm, Application to Protein Family Prediction
[slides]
Assignment #2 released. |
BSA Chapter 5 (through Section 5.4) |
| Wed | Feb 18 | B | Lab #1 | Profile HMM Lab [materials] | ||
| Mon | Feb 23 | B | Unsupervised Training HMMs |
(not on first midterm) Viterbi training, Expectation-Maximization
[slides] |
BSA Section 3.3 | |
| 5 | Wed | Feb 25 | A/B | Midterm #1 Review |
Q&A of course material on midterm exam #1
IMPORTANT: No zoom attendence or recording available |
Study Guide PDF |
| 6 | Mon | Mar 2 | A/B | Midterm Exam #1 | Midterm exam in-class covering modules A and B (except unsupervised training) | |
| Wed | Mar 4 | C | RNA Secondary Structure | RNA secondary structure definition, evolutionary constraints, information degeneracy, psuedoknots, input/output to prediction problem, accuracy calculations | BSA Chapter 10 (through 10.2) | |
| 7 | Mon | Mar 9 | C | Grammars | Grammars, Moore vs. Mealy Machines, Context-Free Grammars (CFGs), Stochastic CFGs | BSA Chapter 9 (skip Section 9.4) |
| Wed | Mar 11 | C | Optimization | Maximium Base Pairs (Nussinov's Algorithm) and Minimum Energy | BSA Section 10.2 through first sub-section on Energy minimization (skip SCFG sub-section) | |
| 8 | Mon | Mar 16 | No class | Spring break | ||
| Wed | Mar 18 | No class | Spring break | |||
| 9 | Mon | Mar 23 | C | Neural Networks | Feedforward neural networks, Cost functions (e.g. cross-entropy), Output units and activation functions (sigmoid, softmax, linear, ReLU), Optimization methods | DL Chapter 6 |
| Wed | Mar 25 | C | UFold | UFold Input / Ouput, Feature Construction, Output Postprocessing | (Optional) Ufold paper, CDPfold paper, E2Efold paper | |
| 10 | Mon | Mar 30 | C | UNet | UNet Encoder / Contraction Path (e.g., Convolution and Max Pool), UNet Decoder / Expansion Path (e.g., Convolution and Upsampling) | DL Chapter 9 |
| Wed | Apr 1 | C | Midterm #2 Review |
Discussion of course material on midterm exam #2
IMPORTANT: No zoom attendence or recording available |
Study Guide PDF | |
| 11 | Mon | Apr 6 | C | Lab #2 | UFold Lab | Lab Manual PDF |
| Wed | Apr 8 | C | Midterm Exam #2 |
Midterm exam in-class covering module C
Also, drop with a W deadline in two days (Fri Apr 10) |
||
| 12 | Mon | Apr 13 | D | Intro to Protein Structures | Amino acids, backbone, side chain, alpha-helix, beta-sheet, phi/psi angles, data banks | ISB Chapter 1 |
| Wed | Apr 15 | D | Protein Secondary Structure Prediction | PhD method (profile + neural network), Multi-class classification, micro-average, macro-average, class imbalance, segment of overlap (SOV) score | ||
| 13 | Mon | Apr 20 | D | Language Models | Attention, Transformers, Masked Language Models (LMs) | (Optional) ESM paper, Attention Is All You Need paper (also see related wikipedia page, blog posts, etc.) |
| Wed | Apr 22 | D | Protein LMs | Experimental Evaluation of pLMs, Categorical Jacobian, Contact Prediction and Self-Attention, Evaluation metrics for contacts and tertiary structure prediction | (Optional) pLMs learn paper | |
| 14 | Mon | Feb 27 | D | Lab #3 | Protein Language Lab | Lab Manual PDF |
| Wed | Apr 29 | D | AlphaFold2 Overview | Alphafold2 Overview, Inputs, and Featurization | (Optional) alphafold2 paper | |
| 15 | Mon | May 4 | D | Evoformer Module | Alphafold2 Initialization of MSA and pair representation, Evoformer, Self-Attention (again), Outer mean product, Triangle updates (with and without self-attention) | (Optional) alphafold2 paper |
| Wed | May 6 | D | Structure Module | Alphafold2 Structure Module Overview, Backbone Frame, Torsion Angles, etc. | (Optional) alphafold2 paper | |
| 16 | Mon | May 11 | A-D | Final Review |
Discussion of course material on final exam
IMPORTANT: No zoom attendence or recording available |
Study Guide PDF |
| Wed | May 13 | No class | Reading day | |||
| Fri | May 15 | Final Exam | Final exam in-class from 10:30am-12:30pm |