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Biomedical Informatics 217: Translational Bioinformatics

BIOMEDIN 217 (otherwise known as Translational Bioinformatics, or CS 275) will be taught virtually by
Dr. Butte for the 2014-2015 Winter Quarter. There are no in-person lectures this year.

Stanford students can register for the class using Axess (https://axess.stanford.edu). Remote SCPD students can register at http://scpd.stanford.edu or call SCPD at 650-725-3016 and ask for student services.

The video lectures for this year will be those given during the 2013-2014 academic year (January to March 2014).

Lectures can be found in the following locations:

  • Stanford-based students should watch the videos via https://myvideosu.stanford.edu.
  • Remote SCPD students need to watch the videos via their myStanfordconnection account off the main SCPD site http://scpd.stanford.edu. SCPD keeps track of the videos watched, so it is important for SCPD students to use this link.

The TAs this year are Emily Tsang etsang@stanford.edu and Daniel Kim danielskim@stanford.edu. If you are taking the class, be sure to sign up for Piazza (http://www.piazza.com) and our Piazza site (http://www.piazza.com/stanford/winter2015/biomedin217).

The TAs will be offering one R/MySQL help session: TBA

We will require four problem sets, one midterm exam, one final exam, and a final project.

Welcome to BMI 217! – Atul Butte

Schedule

Intro

It is the responsibility of those of us involved in today's biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation… At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary.

– Elias A. Zerhouni, M.D.,
Director, National Institutes of Health
New England Journal of Medicine, 353:1621, 2005

Translational Bioinformatics is the development of analytic, storage, and interpretive methods to optimize the transformation of increasingly voluminous genetic, genomic, and biological data into diagnostics and therapeutics for medicine.

Topics covered in this course:

  • Access and utility of publicly available data sources
  • Types of genome-scale measurements in molecular biology and genomic medicine
  • Analysis of microarray data
  • Analysis of polymorphisms, proteomics, and protein interactions
  • Linking genome-scale data to clinical data and phenotypes
  • New questions in biomedicine using bioinformatics. Case studies.

Details

Also known as: Computer Science 275

Time: Lectures delivered virtually, no in-class sessions

First class: Lectures delivered virtually, no in-class sessions

Location: Virtual

Prerequisites: Programming ability at the level of cs106A and familiarity with statistics and biology, or approval of the instructor.

Grading: Grading will be based on four problem sets, midterm exam, final exam, and a final project.

Staff

Instructor: Atul Butte, MD, PhD, Associate Professor of Pediatrics and, by courtesy, Medicine and Computer Science, and two time winner of the American Association for Clinical Chemistry Outstanding Speaker Award. Profile Lab

Teaching Assistants: Emily Tsang etsang@stanford.edu and Daniel Kim danielskim@stanford.edu

Contact: Most questions should be posted to the piazza group page, so that all students can benefit from the answers. Professor Butte or the TAs will respond as soon as possible. For private matters, you can contact us at biomedin217-win1415-staff@lists.stanford.edu. Please do not email the TAs individually unless you specifically have a question for just one of them.

Office Hours:

Audience

This course is designed for:

  • Graduate students in biomedical informatics, genetics, computer science, or other related disciplines
  • Medical students
  • Medical, pediatric, surgical or other fellows with an interest in learning and using bioinformatics in research
  • Interested undergraduates
  • Auditors welcome including medical staff, medical/pediatric/surgical fellows, post-doctoral fellows, and undergraduates. Please contact us to be added to the class email list.

Books

Required books: None (the course will be taught using recent publications)

Recommended books:

  • Genomic and Personalized Medicine, Vol 1-2. Ed. Huntington Willard and Geoffrey Ginsburg. 2008. These two books cover a comprehensive look at “advances in the diagnosis, prevention and treatment of human disease.”
  • Peter Dalgaard. Introductory Statistics with R. 2002. This short book serves as a handbook and tutorial for learning the free statistical system R, used in this course. This book is not specific for biological data analysis. Link E-books at Lane
  • Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. 2005. This large book serves as a comprehensive book on using the Bioconductor libraries in R for biomedical data analysis. Link E-books at Lane
  • Terry Brown. Genomes 3. 2006. A great non-overwhelming reference on molecular biology, with specific focus on novel genome-era measurement tools, such as microarrays. A previous edition of this text is available free at NCBI.]] E-books at Lane

Coursework

Questions

Students should use the piazza group page. To access it, please first subscribe to the BIOMEDIN217 class in http://www.piazza.com. After that this link will direct you to the group. Use it for discussion and posting questions/answers related to the course and problem sets.

Readings

Readings are linked from each lecture on the schedule page. Expect two to three readings per video lecture as preparation.

Problem sets

Four problem sets: hands-on analysis of data, which start with reproducing the findings in one or two publications given their raw data, then adding a twist.

  • Problem Set 1: Integrative genomics, part 1: Genome wide association studies
  • Problem Set 2: Integrative genomics, part 2: Gene Expression, variants, and functional annotation
  • Problem Set 3: Integrative clinomics: Integrating molecular measurements with clinical data.
  • Problem Set 4: Clinical Trial Data & ImmPort

Problem set coding language policy: You must use R and/or MySQL for the problem sets.

Problem set collaboration policy: You cannot talk with others in the class about the problem sets, and you must turn in your own individual work.

Problem set due dates: Problem sets are due at or before 11:59 PM on the due date. You are allotted 4 free late days total this quarter, so use them wisely for the four problem sets. After that, 20% off your grade per day late. Not using all your late days may influence your final grade (bump up) if borderline.

Final project

In the second half of the quarter, the students will research, design, and implement a project of similar scope to one of the problem sets.

Final project details

Midterm Exam

Based on the lectures and the readings. Open book.

Final Exam

Based on all the lectures and the readings. Open book.

Scoring

Midterms and finals may be graded on a curve. Final grades will be calculated:

Component Percent Dates Location
Problem set 1 10% Out January 7th, Due January 14th 11:59 pm Pacific
Problem set 2 10% Out January 14th, Due January 21nd 11:59 pm Pacific
Problem set 3 10% Out January 21nd, Due January 28th 11:59 pm Pacific
Problem set 4 10% Out February 4th, Due February 11th 11:59 pm Pacific
Midterm exam 15% February 2nd, 6:00 - 7:15 pm Alway M114
Final exam 15% March 16th, 12:15 - 1:45 pm Alway M106
Final project proposal 5% Due February 4th, 2015 11:59 pm Pacific
Final project report 25% Due March 13, 2015 11:59 pm Pacific
Total 100%
start.txt · Last modified: 2014/12/18 16:07 by danielskim