skip to main content
10.1145/2020408.2020539acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
abstract

Knowledge discovery and data mining in pharmaceutical cancer research

Published: 21 August 2011 Publication History

Abstract

Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.

Supplementary Material

JPG File (ipe-2-1.jpg)
MP4 File (ipe-2-1.mp4)

Index Terms

  1. Knowledge discovery and data mining in pharmaceutical cancer research

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2011
    1446 pages
    ISBN:9781450308137
    DOI:10.1145/2020408

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 August 2011

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. bio-informatics
    2. computational biology
    3. gene sequencing
    4. micro-arrays
    5. oncology

    Qualifiers

    • Abstract

    Conference

    KDD '11
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

    Upcoming Conference

    KDD '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 486
      Total Downloads
    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 02 Mar 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media