Methodological Foundations of Biomedical Informatics Fall 2015
 
 
The Sackler Institute of Graduate Biomedical Sciences at NYU School of Medicine
 
NYUMC Center for Health Informatics and Bioinformatics
 
NYUMC High Performance Computing Facility
Methodological Foundations of Biomedical Informatics Fall 2015 (BMSC-GA 4449)

Course Directors:
Kelly Ruggles (Kelly.Ruggles@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 Introduction (September 1, 2015 TRB 718 5pm)
Lecturer: Ruggles & Fenyo ( Slides )
Homework (due date: September 8)


Lecture 2 Scientific Programming (September 8, 2015 TRB 718 5pm)
Lecturer: Peskin & Grover ( Slides )
Homework (due date: September 15)

Reading List
  • Best Practices for Scientific Computing by Wilson et al.
  • Linux/HPC
  • Linux tutorial
  • Git


    Lecture 3 Algorithms (September 15, 2015 TRB 718 5pm)
    Lecturer: Peskin

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

    Additional Reading
  • Rosalind, Algorithm Heights
  • Coursera: Algorithms Part I
  • Coursera: Algorithms Part II


    Lecture 4 Statistics (September 22, 2015 TRB 718 5pm)
    Lecturer: Fang ( Slides )

    Reading List
  • All of Statistics by Larry Wasserman, Chapters 1-3
  • Let's Give Statistics the Attention it Deserves
  • Statistics for Biologists

    Additional Reading
  • Think Stats by Allen B. Downey
  • Think Bayes by Allen B. Downey
  • An Introduction to Statistical Modeling of Extreme Values by Stuart Coles
  • All of Nonparametric statistics by Larry Wasserman


    Lecture 5 Linear Algebra (September 29, 2015 TRB 718 5pm)
    Lecturer: Fenyo ( Slides )

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

    Additional Reading
  • Coursera: Coding the Matrix


    Lecture 6 Optimization (October 6, 2015 TRB 718 5pm)
    Lecturer: Fenyo ( Slides )
    Homework (due date: October 16)

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

    Additional Reading
  • Coursera: Linear and Discrete Optimization


    Lecture 7 Data visualization (October 13, 2015 TRB 718 5pm)
    Lecturer: Ruggles ( Slides )
    Homework (due date: October 20)

    Reading List
  • Data visualization: A view of every Points of View column
  • Data Analysis with Open Source Tools by Philipp K. Janert

    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 8 Experimental design (October 20, 2015 TRB 718 5pm)
    Lecturer: Fenyo ( Slides )

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

    Additional Reading
  • Essentials of Clinical Research by Stephen P. Glasser
  • Handbook for Good Clinical Research Practice (GPC - WHO)


    Lecture 9 Information Retrieval (October 27, 2015 TRB 718 5pm)
    Lecturer: Aphinyanaphongs ( Slides )

    Reading List
  • Information Retrieval by William Hersh Chapter 1-2


    Lecture 10 Machine Learning (November 3, 2015 TRB 718 5pm)
    Lecturer: Ma ( Slides )

    Reading List
  • An Introduction to Statistical Learning by Gareth James et al. Chapter 1-2
  • ROC Graphs: Notes and Practical Considerations for Researchers by Tom Fawcett

    Additional Reading
  • Coursera: Machine Learning
  • A Gentle Introduction to Support Vector Machines in Biomedicine: Theory and Methods (Volume 1) by Alexander Statnikov et al.
  • A Gentle Introduction to Support Vector Machines in Biomedicine: Case Studies and Benchmarks (Volume 2) by Alexander Statnikov et al.


    Lecture 11 Signal Processing (November 10, 2015 TRB 718 5pm)
    Lecturer: Fenyo ( Slides )
    Homework (due date: November 24)

    Additional Reading
  • Coursera Digital Signal Processing


    Lecture 12 Pathways and Networks (November 17, 2015 TRB 718 5pm)
    Lecturer: D'Eustachio

    Reading List
  • All of Statistics by Larry Wasserman, Chapters 16-18
  • The Algorithm Design Manual by Steven S Skiena, Chapter 5
  • Pathway and network analysis of cancer genomes

    Additional Reading
  • An Introduction to Systems Biology: Design Principles of Biological Circuits by Uri Alon Chapters 1-4
  • Computational Modelling Of Gene Regulatory Networks - A Primer by Hamid Bolouri
  • Coursera: Probabilistic Graphical Models


    Lecture 13 Modeling and Simulation (November 24, 2015 TRB 718 5pm)
    Lecturer: Fenyo

    Reading List
  • All of Statistics by Larry Wasserman, Chapters 23-24
  • Modeling Complex Systems by Nino Boccara Chapters 1-2

    Additional Reading
  • Evolutionary Dynamics: Exploring the Equations of Life by Martin A. Nowak
  • Coursera: Dynamic Modeling Methods for Systems Biology
  • Monte Carlo Statistical Methods by Robert & Casella


    Lecture 14 Project Presentation (December 15, 2015 TRB 718 5pm)