A community computational challenge to predict the activity of pairs of compounds.

Details

Serval ID
serval:BIB_F647C6B32994
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A community computational challenge to predict the activity of pairs of compounds.
Journal
Nature biotechnology
Author(s)
Bansal M., Yang J., Karan C., Menden M.P., Costello J.C., Tang H., Xiao G., Li Y., Allen J., Zhong R., Chen B., Kim M., Wang T., Heiser L.M., Realubit R., Mattioli M., Alvarez M.J., Shen Y., Gallahan D., Singer D., Saez-Rodriguez J., Xie Y., Stolovitzky G., Califano A.
Working group(s)
NCI-DREAM Community, NCI-DREAM Community
Contributor(s)
Abbuehl J.P., Allen J., Altman R.B., Balcome S., Bansal M., Bell A., Bender A., Berger B., Bernard J., Bieberich A.A., Borboudakis G., Califano A., Chan C., Chen B., Chen T.H., Choi J., Coelho L.P., Costello J.C., Creighton C.J., Dampier W., Davisson V.J., Deshpande R., Diao L., Di Camillo B., Dundar M., Ertel A., Gallahan D., Goswami C.P., Gottlieb A., Gould M.N., Goya J., Grau M., Gray J.W., Heiser L.M., Hejase H.A., Hoffmann M.F., Homicsko K., Homilius M., Hwang W., Ijzerman A.P., Kallioniemi O., Karacali B., Karan C., Kaski S., Kim J., Kim M., Krishnan A., Lee J., Lee Y.S., Lenselink E.B., Lenz P., Li L., Li J., Li Y., Liang H., Mattioli M., Menden M.P., Mpindi J.P., Myers C.L., Newton M.A., Overington J.P., Parkkinen J., Prill R.J., Peng J., Pestell R., Qiu P., Rajwa B., Realubit R., Sadanandam A., Saez-Rodriguez J., Sambo F., Singer D., Stolovitzky G., Sridhar A., Sun W., Tang H., Toffolo G.M., Tozeren A., Troyanskaya O.G., Tsamardinos I., van Vlijmen H.W., Wang T., Wang W., Wegner J.K., Wennerberg K., van Westen G.J., Xia T., Xiao G., Xie Y., Yang J., Yang Y., Yao V., Yuan Y., Zeng H., Zhang S., Zhao J., Zhou J., Zhong R.
ISSN
1546-1696 (Electronic)
ISSN-L
1087-0156
Publication state
Published
Issued date
12/2014
Peer-reviewed
Oui
Volume
32
Number
12
Pages
1213-1222
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.
Keywords
Algorithms, B-Lymphocytes/drug effects, Computer Simulation, Drug Combinations, Drug Synergism, Humans
Pubmed
Web of science
Open Access
Yes
Create date
02/06/2022 9:45
Last modification date
03/06/2022 6:37
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