Methods in Quantitive Biology Fall 2017
 
 
The Sackler Institute of Graduate Biomedical Sciences at NYU School of Medicine
 
Methods in Quantitive Biology (BMSC-GA 4449) and Methodological Foundations of BMI (BMIN-GA 1001) Fall 2017

Course Directors:
Kasthuri Kannan (Kasthuri.Kannan@nyumc.org)
David Fenyö (David@FenyoLab.org)

Course Overview

This course provides an overview of foundational knowledge and essential methods relevant for all areas of biomedical informatics. Students will explore recurring themes and application domains most frequently used in the field. The course will be technical and rigorous, and it will include a number of computer science topics. The course content has been selected by the curriculum committee, and the topics will change over time. The majority of the coursework will be programming assignments and readings.

Learning objectives

The student will learn and understand the most commonly used methodologies in the field of biomedical informatics.

Programming Languages

Learning the following programming languages during the duration of the course is required:

Course Assessment
  • Programming Assignments (40%).
  • Discussions (25%)
  • Final Project (35%)
Lectures

Lecture 1 Course Overview (September 6, 2017 Alexandria West 508 5pm)
Lecturer: Fenyo ( Slides )
Homework (due date: September 11)


Lecture 2 Biomedical Data (September 11, 2017 Alexandria West 508 5pm)
Lecturer: Fenyo ( Slides )

Reading List
  • Goodwin et al., Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 17 (2016) 333-51.
  • Altelaar et al., Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet. 14 (2013) 35-48.
  • Combs CA, Shroff H., Fluorescence Microscopy: A Concise Guide to Current Imaging Methods. Curr Protoc Neurosci. 79 (2017) 2.1.1-2.1.25.
  • Cowie et al., Electronic health records to facilitate clinical research, Clin Res Cardiol. 106 (2017) 19.


    Lecture 3 Data Visualization (September 13, 2017 Alexandria West 508 5pm)
    Lecturer: Ruggles ( Slides )

    Reading List
  • Schroeder et al. Genome Medicine 2013, 5:9
  • Data visualization: A view of every Points of View column

    Additional Reading
  • The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures
  • Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau


    Lecture 4 Data Analysis (September 18, 2017 Alexandria West 508 5pm)
    Lecturer: Ruggles ( Slides )

    Reading List
  • Mertins et al., Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534 (2016) 55-62.
  • Bermudez-Hernandez et al., A Method for Quantifying Molecular Interactions Using Stochastic Modelling and Super-Resolution Microscopy, bioRxiv (2017)
  • Rotmensch et al., Learning a Health Knowledge Graph from Electronic Medical Records. Sci Rep. 7 (2017) 5994


    Lecture 5 Algorithms (September 25, 2017 Alexandria West 508 5pm)
    Lecturer: Kannan ( Slides )

    Reading List
  • The Algorithm Design Manual by Steven S Skiena, Chapters 1-4
  • Visualgo

    Additional Reading
  • Introduction to Algorithms, Third Edition by Thomas H. Cormen
  • Rosalind, Algorithm Heights
  • Coursera: Algorithms Part I
  • Coursera: Algorithms Part II


    Lecture 6 Linear Algebra (September 27, 2017 Alexandria West 508 5pm)
    Lecturer: Kannan ( Slides )

    Reading List
  • Quick Review of Matrix and Real Linear Algebra by KC Border

    Additional Reading
  • Coding the Matrix: Linear Algebra through Applications to Computer Science by Philip N. Klein
  • Linear Algebra and Its Applications, 4th Edition by Gilbert Strang


    Lecture 7 Probability & Distributions (October 2, 2017 Alexandria West 508 5pm)
    Lecturer: Jones


    Lecture 8 Project Plan Presentations (October 16, 2017 Alexandria West 508 5pm)


    Lecture 9 Signal Processing I: Introduction (October 18, 2017 Alexandria West 508 5pm)
    Lecturer: Fenyo ( Slides )

    Additional Reading
  • Coursera Digital Signal Processing


    Lecture 10 Optimization (October 23, 2017 Alexandria West 508 5pm)
    Lecturer: Kannan ( Slides )

    Reading List
  • An Introduction to Optimization Chapers 6-9, 19, 20

    Additional Reading
  • Coursera: Linear and Discrete Optimization


    Lecture 11 Missing Data Imputation (October 30, 2017 Alexandria West 508 5pm)
    Lecturer: Orwitz ( Slides )


    Lecture 12 Signal Processing I: Introduction (cont.) (October 30, 2017 Alexandria West 508 6pm)
    Lecturer: Fenyo ( Slides )
    Homework (due date: November 15)


    Lecture 13 Machine Learning (November 1, 2017 Alexandria West 508 5pm)
    Lecturer: Fenyo ( Slides )

    Additional Reading
  • Introduction to Statistical Learning: with Applications in R. James G, Witten D, Hastie T, Tibshirani R. Springer 2013


    Lecture 14 Signal Processing II: Image Processing (November 6, 2017 Alexandria West 508 5pm)
    Lecturer: Kannan


    Lecture 15 Estimation & Hypothesis Testing (November 8, 2017 Alexandria West 508 5pm)
    Lecturer: Jones


    Lecture 15 Clinical Data (November 15, 2017 Alexandria West 508 5pm)
    Lecturer: Aphinyanaphongs


    Lecture 16 Experimental Design (December 4, 2017 Alexandria West 508 5pm)
    Lecturer: Troxel

    Reading List
  • Bias as a threat to the validity of cancer molecular-marker research by David F. Ransohoff, Nat Rev Cancer 5 (2005) 142-149
  • Adaptive clinical trials in oncology by Donald A. Berry, Nature Reviews Clinical Oncology 9 (2012) 199-207.

    Additional Reading
  • Design and Analysis of Experiments by Douglas C. Montgomery
  • Essentials of Clinical Research by Stephen P. Glasser
  • Handbook for Good Clinical Research Practice (GPC - WHO)


    Lecture 17 Modeling and Simulation (December 6, 2017 Alexandria West 508 5pm)
    Lecturer: Fenyo


    Lecture 18 Project Presentations (December 11, 2017 Alexandria West 508 5pm)


    Lecture 19 Project Presentations (December 13, 2017 Alexandria West 508 5pm)