Trevor Hastie
 Stanford University, USA

  Title
    Regularization Paths for Generalized Linear Models via Coordinate Descent

  Abstract
    We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include l₁(the lasso), l₂(ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.

Ker-Chau Li
 Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
 Department of Statistics, UCLA, USA

  Title
    Liquid Association for Large Scale Gene Expression and Network Studies

  Abstract
    The fast-growing public repertoire of microarray gene expression databases provides individual investigators with unprecedented opportunities to study transcriptional activities for genes of their research interest at no additional cost. Methods such as hierarchical clustering, principal component analysis, gene network and others, have been widely used. They offer biologists valuable genome-wide portraits of how genes are co-regulated in groups. Such approaches have a limitation because it often turns out that the majority of genes do not fall into the detected gene clusters. If one has a gene of primary interest in mind and cannot find any nearby clusters, what additional analysis can be conducted? In this talk, I will show how to address this issue via the statistical notion of liquid association. An online biodata mining system is developed in my lab for aiding biologists to distil information from a web of aggregated genomic knowledgebase and data sources at multi-levels, including gene ontology, protein complexes, genetic markers, drug sensitivity. The computational issue of liquid association and the challenges faced in the context of high p low n problems will be addressed.