Please see last year’s syllabus (with slide packs at the bottom) for previews for this year’s lectures, which will be slightly different. For this year, an updated slide pack will be posted after the lecture. (If it is substantially different from ‘22, an updated video will also be posted.) Video recordings for this year’s lectures can be found in the Media Library tab in Canvas.

Date Topic This year’s lecture Last year’s lecture Comment
1/17 *YALE* Spring term classes begin, 8.20 a.m.      
1/18 Introduction 23i1 22i1  
1/23 DATA 1 - Genomics I 23d1 22d1  
1/25 DATA 2 - Genomics II 23d2 22d2  
1/30 DATA 3 - Proteomics I 23d3 22d3  
2/1 DATA 4 - Proteomics II 23d4 22d4  
2/6 MINING 1 - Personal Genomes + Seq. Comparison + Multi-seq Alignment 23i2a, 23m3, 23m4 22i2a, 22m3, 22m4  
2/8 MINING 2 - Fast Alignment + Variant Calling (incl. a focused section on SVs) 23m5,23m6a 22m5,22m6a,22m6b  
2/13 MINING 3 - Basic Multi-Omics Supervised Mining #1 23m6b,23m7,23m8a 22m6b,22m7,22m8a  
2/15 MINING 4 - Supervised Mining #2 + Unsupervised Mining #1 23m8b,23m8c,23m9a,23m9c 22m8a,22m8b,22m8c,22m9a  
2/20 DATA 5 - Knowledge Representation & Databases 23d5 22d5  
2/22 MINING 5 - Unsupervised Mining #2 + Single-Cell Analysis 23m9d, 23m9e 22m9d  
2/27 Quiz on 1st Half     quiz 1 study guide
3/1 TOPICS 1 - Single-Cell Analysis (continues) + Privacy   22t2  
3/6 TOPICS 2 - Network Analysis   22m10a,22m10b,22m10c,M10d  
3/8 TOPICS 3 - Personal Genomes (from an individual’s perspective)   22i2b  
3/10 Spring break begins      
3/27 TOPICS 4 - Deep Learning Fundamentals   22m12a  
3/29 TOPICS 5 - Deep Learning Fundamentals (continues)   22m12b  
4/3 TOPICS 6 - Practical Machine Learning + Biosensor Data Analysis      
4/5 MODELING 1 - Protein Simulation I   22s1  
4/10 MODELING 2 - Protein Simulation II   22s1, 22s2  
4/12 MODELING 3 - Protein Simulation III   22s2, 22s3  
4/17 MODELING 4 - Markov Models I   22s4  
4/19 MODELING 5 - Markov Models II   22s5  
4/24 Quiz on 2nd Half      
4/26 Final Presentations      
4/28 *YALE* Classes end; Reading period begins      
5/4 *YALE* Final examinations begin      
5/10 *YALE* Final examinations end      

Lecture Slide Pack

See also additional readings for each topic: Additional Readings

# Topic PDF PPT Youtube
(‘21 unless indicated otherwise)
MPEG (2021)
23i1 Introduction to Biomedical Data Science x x I1 I1
23i2a Introduction to Personal Genomes x x I2a  
  An Individual’s Perspective on Personal Genomes     I2b i2b
23d1 DATA - Genomics I x   D1 D1
23d2 DATA - Genomics II x   D2 D2
23d3 DATA - Proteomics I - Proteins x   D3  
23d4 DATA - Proteomics II - Structure x   D4  
23d5 Knowledge Representation & Databases x x D5 D5
23m3 Sequence Comparison x x M3 M3
23m4 Multiple Sequence Comparison x x M4 M4
23m5 Fast Alignment x x M5 M5
23m6a Variant Identification, Focusing on SVs x x M6a M6a
23m6b 1000 Genome + PCAWG summary x x M6b M6b
23m7 Basic Multi-omics (pipeline processing) x x M7 M7
23m8a Supervised Data Mining - Decision Trees x x M8a M8a
23m8b Supervised Data Mining - ROC & Cross-validation x x M8b M8b
23m8c Supervised Data Mining - SVMs x x M8c M8c
23m9a Unsupervised Data Mining - Clustering x x M9a M9a
23m9c Unsupervised Data Mining - SVD x x M9c M9c
23m9d Unsupervised Data Mining - SVD extensions x x M9d M9d
23m9e Single Cell Analysis x x   23m9e
  Networks - Intro     M10a M10a
  Networks - Network Quantities     M10b M10b
  Networks - Network Generation Models     M10c M10c
  Biosensor Analysis       22m11
  Privacy in Biomedical Data Science (esp. Genomic Privacy) x x    
  Deep Learning I     M12a M12a
  Deep Learning II     M12b M12b
  Deep Learning III     M12c M12c
  Protein Folding     S1 S1
  Core Repacking     S2 S2
  NMR Structures     S3 S3
  Intrinsically Disordered Proteins     S4 S4

See lab permissions statement in relation to resusing any of the above material.