/tags/2018-winter/index.xml 2018 Winter - McGill Statistics Seminars

2018 Winter

  • Generalized Sparse Additive Models

    Asad Haris · Jan 19, 2018

    Date: 2018-01-19

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    I will present a unified approach to the estimation of generalized sparse additive models in high dimensional regression problems. Our approach is based on combining structure-inducing and sparsity penalties in a single regression problem. It allows for the use of a large family of structure-inducing penalties: Those characterized by semi-norm constraints. This includes finite dimensional linear subspaces, sobolev and holder classes, classes with bounded total variation, among others. We give an efficient computational algorithm to fit this family of models that easily scales to thousands of observations and features. In addition we develop a framework for proving convergence bounds on these estimators; and show that our estimators converge at the minimax optimal rate under suitable conditions. We also compare the performance of existing methods in an empirical study and discuss directions for future work.

  • Modelling RNA stability for decoding the regulatory programs that drive human diseases

    Hamed Najafabadi · Jan 12, 2018

    Date: 2018-01-12

    Time: 15:30-16:30

    Location: BURN 1205

    Abstract:

    The key determinant of the identity and behaviour of the cell is gene regulation, i.e. which genes are active and which genes are inactive in a particular cell. One of the least understood aspects of gene regulation is RNA stability: genes produce RNA molecules to carry their genetic information – the more stable these RNA molecules are, the longer they can function within the cell, and the less stable they are, the more rapidly they are removed from the pool of active molecules. The cell can effectively switch the genes on and off by regulating RNA stability. However, we do not know which genes are regulated at the RNA stability level, and what factors affect their stability. The focus of our research is development of novel computational methods that enables the measurement of RNA stability and decay rate from functional genomics data, and inference of models that explain how human cells regulate RNA stability. We are particularly interested in how defects in regulation of RNA stability can lead to development and progression of various human diseases, such as cancer.