We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...

This paper relies on a large-scale sample of genotyped individuals linked with detailed register data in Denmark to investigate the context-dependence of genetic influences on human capital formation. We show that the returns to genetic endowments, measured by a polygenic score for educational...

Precision medicine or personalized medicine refers to the customization of medical treatment based on the ability to classify individuals into subpopulations that differ in susceptibility to a particular disease or in response to a specific treatment. This new perception about diagnosis and...

Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a...

This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems...

Recent years have seen astonishing advances in technology and explosions of data in biopharmaceutical R&D, which are driving two waves of change to the industry[...]

This paper relies on a large-scale sample of genotyped individuals linked with detailed register data in Denmark to investigate the context-dependence of genetic influences on human capital formation. We show that the returns to genetic endowments, measured by a polygenic score for educational...