Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Jing Tang

ABSTRACT

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed. More... »

PAGES

485-506

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-1-4939-7154-1_30

    DOI

    http://dx.doi.org/10.1007/978-1-4939-7154-1_30

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090831028

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28730498


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