



Methodological Foundations of Biomedical Informatics Fall 2015 (BMSCGA 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 14
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 13
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 69, 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) 199207.
Bias as a threat to the validity of cancer molecularmarker research by David F. Ransohoff, Nat Rev Cancer 5 (2005) 142149
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 12
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 12
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 1618
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 14
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 2324
Modeling Complex Systems by Nino Boccara Chapters 12
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)


