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Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models.

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Title: Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models.
Authors: Funnell T; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., Zhang AW; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada., Grewal D; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America., McKinney S; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada., Bashashati A; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada., Wang YK; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada., Shah SP; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Source: PLoS computational biology [PLoS Comput Biol] 2019 Feb 22; Vol. 15 (2), pp. e1006799. Date of Electronic Publication: 2019 Feb 22 (Print Publication: 2019).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models.
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  Data: <searchLink fieldCode="AU" term="%22Funnell+T%22">Funnell T</searchLink>; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.<br /><searchLink fieldCode="AU" term="%22Zhang+AW%22">Zhang AW</searchLink>; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.<br /><searchLink fieldCode="AU" term="%22Grewal+D%22">Grewal D</searchLink>; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.<br /><searchLink fieldCode="AU" term="%22McKinney+S%22">McKinney S</searchLink>; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.<br /><searchLink fieldCode="AU" term="%22Bashashati+A%22">Bashashati A</searchLink>; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.<br /><searchLink fieldCode="AU" term="%22Wang+YK%22">Wang YK</searchLink>; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.<br /><searchLink fieldCode="AU" term="%22Shah+SP%22">Shah SP</searchLink>; Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.; Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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  Data: <searchLink fieldCode="JN" term="%22101238922%22">PLoS computational biology</searchLink> [PLoS Comput Biol] 2019 Feb 22; Vol. 15 (2), pp. e1006799. <i>Date of Electronic Publication: </i>2019 Feb 22 (<i>Print Publication: </i>2019).
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