• Login
    View Item 
    •   Home
    • UMass Chan Student Research and Publications
    • Morningside Graduate School of Biomedical Sciences
    • Morningside Graduate School of Biomedical Sciences Scholarly Publications
    • View Item
    •   Home
    • UMass Chan Student Research and Publications
    • Morningside Graduate School of Biomedical Sciences
    • Morningside Graduate School of Biomedical Sciences Scholarly Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of eScholarship@UMassChanCommunitiesPublication DateAuthorsUMass Chan AffiliationsTitlesDocument TypesKeywordsThis CollectionPublication DateAuthorsUMass Chan AffiliationsTitlesDocument TypesKeywords

    My Account

    LoginRegister

    Help

    AboutSubmission GuidelinesData Deposit PolicySearchingAccessibilityTerms of UseWebsite Migration FAQ

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Integrating high-content screening and ligand-target prediction to identify mechanism of action

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Young, Daniel W.
    Bender, Andreas
    Hoyt, Jonathan
    McWhinnie, Elizabeth
    Chirn, Gung-Wei
    Tao, Charles Y.
    Tallarico, John A.
    Labow, Mark A.
    Jenkins, Jeremy L.
    Mitchison, Timothy J.
    Feng, Yan
    Show allShow less
    UMass Chan Affiliations
    Department of Cell Biology
    Document Type
    Journal Article
    Publication Date
    2007-12-11
    Keywords
    *Antineoplastic Agents; Cell Cycle; Cell Nucleus; Cell Proliferation; Cluster Analysis; Computational Biology; DNA Replication; Dose-Response Relationship, Drug; *Drug Design; Hela Cells; Humans; Ligands; Models, Statistical; Molecular Structure; Predictive Value of Tests; Protein Binding; *Small Molecule Libraries; Structure-Activity Relationship
    Life Sciences
    Medicine and Health Sciences
    
    Metadata
    Show full item record
    Link to Full Text
    http://dx.doi.org/10.1038/nchembio.2007.53
    Abstract
    High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.
    Source
    Nat Chem Biol. 2008 Jan;4(1):59-68. Epub 2007 Dec 9. Link to article on publisher's site
    DOI
    10.1038/nchembio.2007.53
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/32865
    PubMed ID
    18066055
    Related Resources
    Link to Article in PubMed
    ae974a485f413a2113503eed53cd6c53
    10.1038/nchembio.2007.53
    Scopus Count
    Collections
    Morningside Graduate School of Biomedical Sciences Scholarly Publications

    entitlement

    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Lamar Soutter Library, UMass Chan Medical School | 55 Lake Avenue North | Worcester, MA 01655 USA
    Quick Guide | escholarship@umassmed.edu
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.