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Personalized-medicine

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Tel Aviv University -- Blavatnik School of Computer Science

Seminar 0368-3178-01

Towards the Precision Medicine Era: Computational challenges​

: :

http://www.cs.tau.ac.il/~rshamir/seminar/16/precmedsem16.html

Prof. Ron Shamir

Fall 2016

Tuesdays 3-5 pm, Shenkar Physics bldg. 105

 

Topic: The era of personalized (or precision) medicine is behind the corner. The combination of cheap and accessible biotechnology, advanced computation and Big Data is expected to change the way drugs and treatments are administered to patients: rather than one-size-fits-all, they will be tailored to the particular properties of a group of individuals. These properties can be based on their genomes (available via DNA deep sequencing), their life style (known via online monitoring and wearable devices) and their medical history (available as electronic medical records). This tailoring, i.e., the determination of the best treatment based on the parameters, raises major computational challenges, and we shall aim to study some of those in the seminar.

Large projects around the world have been initiated over the past few years towards that era. Among those are President Obama's Precision Medicine Initiative in the US, Genomics England 100,000 Genomes Project, and Denmark's GenomeDenmark platform. Commercial companies like 23andme and Regeneron and Geisinger Health Systems have collected genetic and clinical data from hundreds of thousands of patients. The grand challenge is how to make the best use of such data.

The techniques in the papers that we shall discuss come from the areas of algorithms, statistics and machine learning. A big part of the learning effort will be to understand the difficulties and peculiarities of the specific data types.

The seminar is open for BSc AND MSc students. Among master students, those in the bioinformatics track will have priority. Other interested students should inquire with the instructor.

Prerequisites: Passing successfully the courses Statistics for CS and Algorithms. Background in machine learning and bioinformatics is advantage but not a must. The basic background in biology will be given in the first meetings.

Course material:

       Syllabus (tentative)

       Guideline to speakers

Plan

Note that some topics in the syllabus have multiple papers. Only the first is listed here.

Date

Speaker

Topic

Paper

Method

Presentation

1/11

Ron Shamir

Introduction - biology

Lec 1 and 2

8/11

Ron Shamir

Introduction precision medicine

 

15/11

Nimrod Rapoport

Breast cancer subclasses

Netanely (16)

K-means, FDR, Kaplan-Meyer plots, log-rank test, Cox uni and multivariate analysis, Wilcoxon rank sum test

Lec 3

21/11

Ron Shamir

Mammaprint Breast cancer prognosis biomarkers

Van't veer (02)

Clustering, classification

Lec 4

29/11

No meeting

 

 

6/12

Gal Dinstag

patient specific driver gene prediction

Nagarajan (15)

OncoImpact , shortest paths, randomization testing

Lec 5

13/12

Ron Gal

Pan cancer analysis

Leiserson (15)

Insulated heat diffusion process

Lec 6

 

20/12

Zohar Manber

Phenome-wide scan of gene-disease associations

Denny (10, 13)

Association, p-value, chi-square tests, multiple hypothesis corrections

Lec 7

27/12

Nomi Hadar

Personalized prediction of glycemic response

Segal (15)

Gradient boosting regression, decision trees, partial dependence plots

Lec 8

3/1/17

Chen Arviv

Autism classification

Lingren (16)

Rule-based classification, SVM, clust4ering, dimension reduction

Lec 9

 

 

 

 

 

 

 

 

 

 

 


 

 

Contact info: email: rshamir AT tau dot ac dot il; phone: 640-5383; office: Schreiber 014; office hours by appointment

 

 

picture credits:

          http://bioinformaticsreview.com/20151005/biominer-intro/

          Time magazine

          https://www.linkedin.com/pulse/20140923215637-5241481-artificial-intelligence-to-deliver-personalised-medicine

         https://www.whitehouse.gov/blog/2015/01/30/precision-medicine-initiative-data-driven-treatments-unique-your-own-body

 

 

 

 

 

 

 

 

 

 

 

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