Methods in Quantitative Biology Fall 2018
 
 
The Sackler Institute of Graduate Biomedical Sciences at NYU School of Medicine
 
Methods in Quantitative Biology (BMSC-GA 4449) and Methodological Foundations of BMI (BMIN-GA 1001) Fall 2018

Course Directors:
Kelly Ruggles (Kelly.Ruggles@nyumc.org)
David Fenyö (David@FenyoLab.org)
Teaching Assistant:
Cooper Devlin (jcooperdevlin@gmail.com)

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

Working knowledge of R and Python is required:

Course Assessment
  • Programming Assignments (40%).
  • Discussions (40%)
  • Participation (20%)
Lectures

Lecture 1 Introduction & Best Practices (September 5, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
Lecturer: Devlin & Ruggles ( Slides )


Lecture 2 HPC (September 7, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 2-3pm)
Lecturer: Costantino ( Slides )


Lecture 3 Biomedical Data I (September 12, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
Lecturer: Ruggles ( Slides )

Reading List
  • Karczewski and Snyder. Integrative omics for health and disease. (2018) Nature Reviews


    Lecture 4 Biomedical Data I - Discussion (September 14, 2018 SB 920, TRB 718, TRB 819 2-3pm)
    Lecturer: Ruggles

    Reading List
  • Discussion Paper: Hoadley et al., Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. (2018) Cell


    Lecture 5 Biomedical Data II (September 19, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Testa ( Slides )

    Reading List
  • Coorevits . et al., Electronic health records: new opportunities for clinical research. (2013) Journal of Internal Medicine


    Lecture 6 Signal Processing I (September 26, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Fenyo ( Slides )


    Lecture 7 Signal Processing - Lab (September 28, 2018 Science Building, 345 East 30th St, 1st Floor, Room SB 103 2-3pm)
    Lecturer: Fenyo ( Slides )

    Reading List
  • Assignment: Add comments to the signal processing code provided describing what is being done in the code.
  • Sun X, Wang X, et al.: "Transcription factor profiling reveals molecular choreography and key regulators of human retrotransposon expression", Proc Natl Acad Sci U S A. 2018 May 25. pii: 201722565


    Lecture 8 Signal Processing II (October 10, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Fenyo ( Slides )


    Lecture 9 Signal Processing II - Discussion (October 12, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 2-3pm)
    Lecturer: Fenyo

    Reading List
  • Reuben Thomas, Sean Thomas, Alisha K. Holloway and Katherine S. Pollard, Features that define the best ChIP-seq peak calling algorithms, Briefings in Bioinformatics, 18(3), 2017, 441–450


    Lecture 10 Modeling/Simulation I (October 24, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Fenyo ( Slides )


    Lecture 11 Modeling/Simulation I - Discussion (October 26, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 2-3pm)
    Lecturer: Ruggles

    Reading List
  • Discussion Paper: Gao et al., Prevention and Control of Zika as Mosquito-Borne and Sexually Transmitted Disease: A Mathematical Modeling Analysis.(2016) Scientific Reports


    Lecture 12 Modeling/Simulation I - Discussion (November 2, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 2-3pm)
    Lecturer: Ruggles

    Reading List
  • Discussion Papers:
  • Ginsberg et al.: Detecting influenza epidemics using search engine query data, Nature 457 (2008) 1012–1014
  • Lazer et al.: The Parable of Google Flu: Traps in Big Data Analysis.” Science, 343 (2014) 1203-1205


    Lecture 13 Data Visualization - Discussion (November 7, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Ruggles

    Reading List
  • Nature Methods Points of View
  • Assignment: Based on best-practices described by the reading, find a figure from a publication that you feel is particularly good. Each student will present 2 minutes on why their figure is the best choice and students will vote on their top pick.


    Lecture 14 Data Visualization - ggplot2 Lab (November 9, 2018 Medical Science Building, 2nd Floor, Room 248 2-3pm)
    Lecturer: Devlin & Ruggles

    Reading List
  • Assignment: TBD


    Lecture 15 Data Visualization - Illustrator Lab (November 14, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 5-7pm)
    Lecturer: Ruggles

    Reading List
  • Assignment: Given this figure, improve the formatting and layout of the figure and add a schematic drawing of the associated cellular pathway.


    Lecture 16 Data Visualization - Interactive (November 27, 2018 Science Building, 345 East 30th St, Ground Floor, Room G19 4-6pm)
    Lecturer: Askenazi

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

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