Predicting cancer drug response: Advancing the dream

Cancer Discovery

Volumn 5, Issue 3, 2015, Pages 237-238

  Altman, Russ B ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DRUG POTENTIATION; DRUG RESPONSE; DRUG SENSITIVITY; EPIGENETICS; GENE EXPRESSION; GENOMICS; HUMAN; MACHINE LEARNING; NEOPLASM; PROTEIN FUNCTION; PROTEOMICS; STRUCTURE ANALYSIS; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; PROCEDURES; PROTEIN CONFORMATION;

PROTEIN;

COMPUTATIONAL BIOLOGY; MODELS, MOLECULAR; PROTEIN CONFORMATION; PROTEINS;

EID: 84925340868     PISSN: 21598274     EISSN: 21598290     Source Type: Journal    
DOI: 10.1158/2159-8290.CD-15-0093     Document Type: Article

References (5)

1. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr, et al. A whole-cell computational model predicts phenotype from genotype. Cell 2012;150:389–401.
2. Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 2014;32:1202–12.
3. Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, et al. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol 2014;32:1213–22.
4. Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)–round x. Proteins 2014;82(Suppl 2):1–6.
5. Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, et al. A large-scale evaluation of computational protein function prediction. Nat Methods 2013;10:221–7.