Over the past decade, Determinantal Point Processes (DPPs) have proven to be a mathematically elegant framework for modeling diversity. Given a set of items N, DPPs define a probability distribution over subsets of N, with sets of larger diversity having greater probability. Recently, DPPs have achieved success in the domain of recommendation systems, as a method to enforce diversity of recommendations in addition to relevance. In large-scale recommendation applications however, the input typically comes in the form of a stream too large to fit into main memory. However, the natural greedy algorithm for DPP-based recommendations is memory intensive, and cannot be used in a streaming setting.

MOTIVATION: There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues. RESULTS: We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses. AVAILABILITY AND IMPLEMENTATION: Source code is at https://github.com/datduong/RECOV . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Recently, investigators have proposed state-of-the-art Identity-by-descent (IBD) mapping methods to detect IBD segments between purportedly unrelated individuals. The IBD information can then be used for association testing in genetic association studies. One approach for this IBD association testing strategy is to test for excessive IBD between pairs of cases ('pairwise method'). However, this approach is inefficient because it requires a large number of permutations. Moreover, a limited number of permutations define a lower bound for P-values, which makes fine-mapping of associated regions difficult because, in practice, a much larger genomic region is implicated than the region that is actually associated. RESULTS: In this article, we introduce a new pairwise method 'Fast-Pairwise'. Fast-Pairwise uses importance sampling to improve efficiency and enable approximation of extremely small P-values. Fast-Pairwise method takes only days to complete a genome-wide scan. In the application to the WTCCC type 1 diabetes data, Fast-Pairwise successfully fine-maps a known human leukocyte antigen gene that is known to cause the disease. AVAILABILITY: Fast-Pairwise is publicly available at: http://genetics.cs.ucla.edu/graphibd.

MOTIVATION: Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger datasets are used to increase power. RESULTS: We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computational speedup for two random-effects models that makes this approach feasible even for extremely large cohorts. Using this model with both the likelihood ratio test and score test, we find that the former yields more power while controlling type I error. Application of our approach to richly structured Genetic Analysis Workshop 14 data demonstrates that our method successfully corrects for population structure and family relatedness, whereas application of our method to a 15 000 individual Crohn's disease case-control cohort demonstrates that it additionally recovers genes not recoverable by univariate analysis. AVAILABILITY: A Python-based library implementing our approach is available at http://mscompbio.codeplex.com.

There have been many efforts to identify causal graphical features such as directed edges between random variables from observational data. Recently, Tian et al. proposed a new dynamic programming algorithm which computes marginalized posterior probabilities of directed edge features over all the possible structures in O(n3n) time when the number of parents per node is bounded by a constant, where n is the number of variables of interest. However the main drawback of this approach is that deciding a single appropriate threshold for the existence of the directed edge feature is difficult due to the scale difference of the posterior probabilities between the directed edges forming v-structures and the directed edges not forming v-structures. We claim that computing posterior probabilities of both adjacencies and v-structures is necessary and more effective for discovering causal graphical features, since it allows us to find a single appropriate decision threshold for the existence of the feature that we are testing. For efficient computation, we provide a novel dynamic programming algorithm which computes the posterior probabilities of all of n(n – 1)/2 adjacency and n(n–1 choose 2) v-structure features in O(n3 * 3n) time.

We describe a machine-learning-based approach for extracting attribute labels from Web form interfaces. Having these labels is a requirement for several techniques that attempt to retrieve and integrate data that reside in online databases and that are hidden behind form interfaces, including schema matching and clustering, and hidden-Web crawlers. Whereas previous approaches to this problem have relied on heuristics and manually specified extraction rules, our technique makes use of learning classifiers to identify form labels. Our preliminary experiments show this approach is promising and has high accuracy.