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The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from conventional approaches may not be…
Genomic phenotypes, such as DNA methylation and chromatin accessibility, can be used to characterize the transcriptional and regulatory activity of DNA within a cell. Recent technological advances have made it possible to measure such…
We consider a setting in which we have a treatment and a large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between…
Alzheimer's disease (AD) is a serious neurodegenerative disease consisting of four stages where the illness gets progressively worse. It is of great significance to detect the gene regulatory mechanism as AD progresses and, thus, to help us…
Genetic regulatory networks control ontogeny. For fifty years Boolean networks have served as models of such systems, ranging from ensembles of random Boolean networks as models for generic properties of gene regulation to working dynamical…
Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex diseases. Motivated by GWASs' limitations in identifying small effect loci to understand complex traits'…
Disease occurs due to aberrant expression of genes and modulation of the biological pathways along which they lie. Inference of activated gene pathways, using gene expression data during disease progression, is an important problem. In this…
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry…
Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. It is widely believed that these alterations follow combinatorial patterns that have a strong connection with the underlying…
Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph…
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when…
Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their…
Many common diseases are highly polygenic, modulated by a large number genetic factors with small effects on susceptibility to disease. These small effects are difficult to map reliably in genetic association studies. To address this…
Under complete linkage disequilibrium (LD), robust tests often have greater power than Pearson's chi-square test and trend tests for the analysis of case-control genetic association studies. Robust statistics have been used in…
Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies.…
By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value…
Cancer is a heterogeneous disease with different combinations of genetic and epigenetic alterations driving the development of cancer in different individuals. While these alterations are believed to converge on genes in key cellular…
Genetic association studies, in particular the genome-wide association study design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits. The next challenge consists of understanding the…
Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, differentiation of direct from indirect relationships, and hidden…
Sharing genome data in a privacy-preserving way stands as a major bottleneck in front of the scientific progress promised by the big data era in genomics. A community-driven protocol named genomic data-sharing beacon protocol has been…