Machine learning in computational biology

Jean-Philippe Vert

ENSAE
Spring 2017

Modern technologies like DNA microarrays or high-throughput sequencing are revolutionising biology and medical research. By allowing the collection of large amounts of measures at the molecular level on living organisms, they pave the way to a quantitative and rationale analysis of biological systems. Unsurprisingly, statistics and machine learning play an important role in this revolution. By processing large collections of datasets, they allow to extract new biological knowledge and infer predictive models.

The goal of this course is to present a few modern statistical learning techniques, and to touch upon a selected panel of applications in computational and systems biology. We will study in particular support vector machines (SVM) and kernels, as well as feature selection techniques including lasso regression. Applications include protein annotation, virtual screening in drug design, prognostic and predictive models for personalised medicine in oncology, and gene network inference in systems biology.

Results

Schedule

Project

1. Propose and test different techniques to predict the relapse from gene expression data. Check the effect of parameters, estimate the performance.
2. Make a prediction of relapse for the following 92 samples: xtest.txt. You should send me a file with 92 lines, each line containing a score which should be large if you think the corresponding sample has label +1 (relapse), small otherwise.

Please send report and prediction to jean-philippe.vert@mines-paristech.fr before May 10, 2017. The predictions will be scored in terms of area under the ROC curve (AUC).

Although discussions are allowed and encouraged, each student must work on the challenge individually and submit a report and a prediction.