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

Détails

ID Serval
serval:BIB_F647C6B32994
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A community computational challenge to predict the activity of pairs of compounds.
Périodique
Nature biotechnology
Auteur⸱e⸱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.
Collaborateur⸱rice⸱s
NCI-DREAM Community, NCI-DREAM Community
Contributeur⸱rice⸱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
Statut éditorial
Publié
Date de publication
12/2014
Peer-reviewed
Oui
Volume
32
Numéro
12
Pages
1213-1222
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
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.
Mots-clé
Algorithms, B-Lymphocytes/drug effects, Computer Simulation, Drug Combinations, Drug Synergism, Humans
Pubmed
Web of science
Open Access
Oui
Création de la notice
02/06/2022 9:45
Dernière modification de la notice
03/06/2022 6:37
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