Institution
Broad Institute
Nonprofit•Cambridge, Massachusetts, United States•
About: Broad Institute is a nonprofit organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 6584 authors who have published 11618 publications receiving 1522743 citations. The organization is also known as: Eli and Edythe L. Broad Institute of MIT and Harvard.
Papers published on a yearly basis
Papers
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A. Gordon Robertson1, Jaegil Kim2, Hikmat Al-Ahmadie3, Joaquim Bellmunt4 +167 more•Institutions (16)
TL;DR: An analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms identified 5 expression subtypes that may stratify response to different treatments and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology.
1,638 citations
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Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of Vermont4, University of Toronto5, Boston University6, University of Maryland, Baltimore7, Duke University8, University of Kansas9, King's College London10, Columbia University11, Broad Institute12, Purdue University13, University of Iowa14, University of Georgia15, Texas A&M University16, Oklahoma State University–Stillwater17, University of Groningen18, Florida State University19, Uniformed Services University of the Health Sciences20, Bryn Mawr College21, University of North Texas22, University of Otago23, University at Buffalo24, University of Arizona25, University of New South Wales26, Northwestern University27, Emory University28, University of Kentucky29, University of Pittsburgh30, Brown University31
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record
1,635 citations
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TL;DR: Exome and genome sequences and whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases, which are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.
1,631 citations
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TL;DR: This study presents a general framework for deciphering cis-regulatory connections and their roles in disease, and defines multi-cell activity profiles for chromatin state, gene expression, regulatory motif enrichment, and regulator expression.
Abstract: Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease single nucleotide polymorphisms are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus suggesting a mechanism for the association. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease.
1,624 citations
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Washington University in St. Louis1, Johns Hopkins University2, Discovery Institute3, Genome Institute of Singapore4, University of Miami5, Broad Institute6, Harvard University7, University of Texas MD Anderson Cancer Center8, Memorial Sloan Kettering Cancer Center9, Cornell University10, University of Cambridge11, Catalan Institution for Research and Advanced Studies12, University of California, Santa Cruz13, Baylor College of Medicine14
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.
1,623 citations
Authors
Showing all 7146 results
Name | H-index | Papers | Citations |
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Eric S. Lander | 301 | 826 | 525976 |
Albert Hofman | 267 | 2530 | 321405 |
Frank B. Hu | 250 | 1675 | 253464 |
David J. Hunter | 213 | 1836 | 207050 |
Kari Stefansson | 206 | 794 | 174819 |
Mark J. Daly | 204 | 763 | 304452 |
Lewis C. Cantley | 196 | 748 | 169037 |
Matthew Meyerson | 194 | 553 | 243726 |
Gad Getz | 189 | 520 | 247560 |
Stacey Gabriel | 187 | 383 | 294284 |
Stuart H. Orkin | 186 | 715 | 112182 |
Ralph Weissleder | 184 | 1160 | 142508 |
Chris Sander | 178 | 713 | 233287 |
Michael I. Jordan | 176 | 1016 | 216204 |
Richard A. Young | 173 | 520 | 126642 |