Institution
Institute for Systems Biology
Nonprofit•Seattle, Washington, United States•
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.
Topics: Population, Proteomics, Proteome, Systems biology, Gene
Papers published on a yearly basis
Papers
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TL;DR: The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data, and successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data.
Abstract: We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.
551 citations
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Washington University in St. Louis1, Baylor College of Medicine2, Xi'an Jiaotong University3, Ontario Institute for Cancer Research4, Institute for Systems Biology5, Broad Institute6, University of Texas MD Anderson Cancer Center7, Mayo Clinic8, Kuwait University9, University of Toronto10, Princeton University11, Wake Forest University12
TL;DR: The largest investigation of predisposition variants in cancer to date finds 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types, informing future guidelines of variant classification and germline genetic testing in cancer.
543 citations
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TL;DR: The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator.
535 citations
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Natural Environment Research Council1, European Bioinformatics Institute2, Wellcome Trust Sanger Institute3, University of Cambridge4, Stanford University5, Swiss Institute of Bioinformatics6, University of British Columbia7, Livestrong Foundation8, Institute for Systems Biology9, University of California, Davis10, Lockheed Martin Corporation11, University of Edinburgh12, Newcastle University13, Medical Research Council14, Aberystwyth University15, National Science Foundation16, Beilstein-Institut17, National Institutes of Health18, Boston Children's Hospital19, Norwegian University of Science and Technology20, University of Georgia21, University of California, Berkeley22, University of Texas Southwestern Medical Center23, Lancaster University24, German Cancer Research Center25, University of Manchester26, Harvard University27, Iowa State University28, Bristol-Myers Squibb29, University at Buffalo30, AstraZeneca31, Trinity College, Dublin32, Wageningen University and Research Centre33, Ghent University34
TL;DR: The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.
Abstract: The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.
535 citations
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TL;DR: The PeptideAtlas Project is described, its contents and components are described, and it is shown how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.
Abstract: A crucial part of a successful systems biology experiment is an assay that provides reliable, quantitative measurements for each of the components in the system being studied. For proteomics to be a key part of such studies, it must deliver accurate quantification of all the components in the system for each tested perturbation without any gaps in the data. This will require a new approach to proteomics that is based on emerging targeted quantitative mass spectrometry techniques. The PeptideAtlas Project comprises a growing, publicly accessible database of peptides identified in many tandem mass spectrometry proteomics studies and software tools that allow the building of PeptideAtlas, as well as its use by the research community. Here, we describe the PeptideAtlas Project, its contents and components, and show how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.
534 citations
Authors
Showing all 1292 results
Name | H-index | Papers | Citations |
---|---|---|---|
Younan Xia | 216 | 943 | 175757 |
Ruedi Aebersold | 182 | 879 | 141881 |
David Haussler | 172 | 488 | 224960 |
Steven P. Gygi | 172 | 704 | 129173 |
Nahum Sonenberg | 167 | 647 | 104053 |
Leroy Hood | 158 | 853 | 128452 |
Mark H. Ellisman | 117 | 637 | 55289 |
Wei Zhang | 112 | 1189 | 93641 |
John Ralph | 109 | 442 | 39238 |
Eric H. Davidson | 106 | 454 | 47058 |
James R. Heath | 103 | 425 | 58548 |
Alan Aderem | 99 | 246 | 46682 |
Anne-Claude Gingras | 97 | 336 | 40714 |
Trey Ideker | 97 | 306 | 72276 |
Michael H. Gelb | 94 | 506 | 34714 |