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Krishnan troyanskaya

Arjun Krishnan, Ran Zhang, Victoria Yao, Chandra L Theesfeld, Aaron K Wong, Alicja Tadych, Natalia Volfovsky, Alan Packer, Alex Lash, Olga G Troyanskaya
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence...
August 1, 2016: Nature Neuroscience
Aaron K Wong, Arjun Krishnan, Victoria Yao, Alicja Tadych, Olga G Troyanskaya
IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data...
July 1, 2015: Nucleic Acids Research
Jonathan Goya, Aaron K Wong, Victoria Yao, Arjun Krishnan, Max Homilius, Olga G Troyanskaya
Functional Networks of Tissues in Mouse (FNTM) provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function...
July 1, 2015: Nucleic Acids Research
Casey S Greene, Arjun Krishnan, Aaron K Wong, Emanuela Ricciotti, Rene A Zelaya, Daniel S Himmelstein, Ran Zhang, Boris M Hartmann, Elena Zaslavsky, Stuart C Sealfon, Daniel I Chasman, Garret A FitzGerald, Kara Dolinski, Tilo Grosser, Olga G Troyanskaya
Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases...
June 2015: Nature Genetics
Maria D Chikina, Christophe P Gerald, Xianting Li, Yongchao Ge, Hanna Pincas, Venugopalan D Nair, Aaron K Wong, Arjun Krishnan, Olga G Troyanskaya, Deborah Raymond, Rachel Saunders-Pullman, Susan B Bressman, Zhenyu Yue, Stuart C Sealfon
The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers...
May 2015: Movement Disorders: Official Journal of the Movement Disorder Society
Qian Zhu, Aaron K Wong, Arjun Krishnan, Miriam R Aure, Alicja Tadych, Ran Zhang, David C Corney, Casey S Greene, Lars A Bongo, Vessela N Kristensen, Moses Charikar, Kai Li, Olga G Troyanskaya
We present SEEK (search-based exploration of expression compendia;, a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options...
March 2015: Nature Methods
Christopher Y Park, Arjun Krishnan, Qian Zhu, Aaron K Wong, Young-Suk Lee, Olga G Troyanskaya
MOTIVATION: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses...
April 1, 2015: Bioinformatics
Young-suk Lee, Arjun Krishnan, Qian Zhu, Olga G Troyanskaya
MOTIVATION: Leveraging gene expression data through large-scale integrative analyses for multicellular organisms is challenging because most samples are not fully annotated to their tissue/cell-type of origin. A computational method to classify samples using their entire gene expression profiles is needed. Such a method must be applicable across thousands of independent studies, hundreds of gene expression technologies and hundreds of diverse human tissues and cell-types. RESULTS: We present Unveiling RNA Sample Annotation (URSA) that leverages the complex tissue/cell-type relationships and simultaneously estimates the probabilities associated with hundreds of tissues/cell-types for any given gene expression profile...
December 1, 2013: Bioinformatics
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