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

Mukesh Bansal, Jichen Yang, Charles Karan, Michael P. Menden, James C. Costello, Hao Tang, Guanghua Xiao, Yajuan Li, Jeffrey Allen, Rui Zhong, Beibei Chen, Minsoo Kim, Tao Wang, Laura M. Heiser, Ronald Realubit, Michela Mattioli, Mariano J. Alvarez, Yao Shen, Daniel Gallahan, Dinah Singer & 80 others Julio Saez-Rodriguez, Yang Xie, Gustavo Stolovitzky, Andrea Califano, Jean Paul Abbuehl, Russ B. Altman, Shawn Balcome, Ana Bell, Andreas Bender, Bonnie Berger, Jonathan Bernard, Andrew A. Bieberich, Giorgos Borboudakis, Christina Chan, Ting Huei Chen, Jaejoon Choi, Luis Pedro Coelho, Chad J. Creighton, Will Dampier, V. Jo Davisson, Raamesh Deshpande, Lixia Diao, Barbara Di Camillo, Murat Dundar, Adam Ertel, Chirayu P. Goswami, Assaf Gottlieb, Michael N. Gould, Jonathan Goya, Michael Grau, Joe W. Gray, Hussein A. Hejase, Michael F. Hoffmann, Krisztian Homicsko, Max Homilius, Woochang Hwang, Adriaan P. Ijzerman, Olli Kallioniemi, Bilge Karacali, Samuel Kaski, Junho Kim, Arjun Krishnan, Junehawk Lee, Young Suk Lee, Eelke B. Lenselink, Peter Lenz, Lang Li, Jun Li, Han Liang, John Patrick Mpindi, Chad L. Myers, Michael A. Newton, John P. Overington, Juuso Parkkinen, Robert J. Prill, Jian Peng, Richard Pestell, Peng Qiu, Bartek Rajwa, Anguraj Sadanandam, Francesco Sambo, Arvind Sridhar, Wei Sun, Gianna M. Toffolo, Aydin Tozeren, Olga G. Troyanskaya, Ioannis Tsamardinos, Herman W T Van Vlijmen, Wen Wang, Joerg K. Wegner, Krister Wennerberg, Gerard J P Van Westen, Tian Xia, Yang Yang, Victoria Yao, Yuan Yuan, Haoyang Zeng, Shihua Zhang, Junfei Zhao, Jian Zhou

    Research output: Contribution to journalArticle

    • 48 Citations

    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.

    Original languageEnglish (US)
    Pages (from-to)1213-1222
    Number of pages10
    JournalNature Biotechnology
    Volume32
    Issue number12
    DOIs
    StatePublished - Dec 1 2014

    Profile

    Computer Simulation
    Drug Combinations
    Transcriptome
    B-Lymphocytes
    Dental Materials
    Castration
    Gene expression
    Screening
    Cells

    ASJC Scopus subject areas

    • Applied Microbiology and Biotechnology
    • Biotechnology
    • Molecular Medicine
    • Bioengineering
    • Biomedical Engineering

    Cite this

    Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., ... Zhou, J. (2014). A community computational challenge to predict the activity of pairs of compounds. Nature Biotechnology, 32(12), 1213-1222. DOI: 10.1038/nbt.3052

    A community computational challenge to predict the activity of pairs of compounds. / Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P.; Costello, James C.; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M.; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J.; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea; Abbuehl, Jean Paul; Altman, Russ B.; Balcome, Shawn; Bell, Ana; Bender, Andreas; Berger, Bonnie; Bernard, Jonathan; Bieberich, Andrew A.; Borboudakis, Giorgos; Chan, Christina; Chen, Ting Huei; Choi, Jaejoon; Coelho, Luis Pedro; Creighton, Chad J.; Dampier, Will; Davisson, V. Jo; Deshpande, Raamesh; Diao, Lixia; Di Camillo, Barbara; Dundar, Murat; Ertel, Adam; Goswami, Chirayu P.; Gottlieb, Assaf; Gould, Michael N.; Goya, Jonathan; Grau, Michael; Gray, Joe W.; Hejase, Hussein A.; Hoffmann, Michael F.; Homicsko, Krisztian; Homilius, Max; Hwang, Woochang; Ijzerman, Adriaan P.; Kallioniemi, Olli; Karacali, Bilge; Kaski, Samuel; Kim, Junho; Krishnan, Arjun; Lee, Junehawk; Lee, Young Suk; Lenselink, Eelke B.; Lenz, Peter; Li, Lang; Li, Jun; Liang, Han; Mpindi, John Patrick; Myers, Chad L.; Newton, Michael A.; Overington, John P.; Parkkinen, Juuso; Prill, Robert J.; Peng, Jian; Pestell, Richard; Qiu, Peng; Rajwa, Bartek; Sadanandam, Anguraj; Sambo, Francesco; Sridhar, Arvind; Sun, Wei; Toffolo, Gianna M.; Tozeren, Aydin; Troyanskaya, Olga G.; Tsamardinos, Ioannis; Van Vlijmen, Herman W T; Wang, Wen; Wegner, Joerg K.; Wennerberg, Krister; Van Westen, Gerard J P; Xia, Tian; Yang, Yang; Yao, Victoria; Yuan, Yuan; Zeng, Haoyang; Zhang, Shihua; Zhao, Junfei; Zhou, Jian.

    In: Nature Biotechnology, Vol. 32, No. 12, 01.12.2014, p. 1213-1222.

    Research output: Contribution to journalArticle

    Bansal, M, Yang, J, Karan, C, Menden, MP, Costello, JC, Tang, H, Xiao, G, Li, Y, Allen, J, Zhong, R, Chen, B, Kim, M, Wang, T, Heiser, LM, Realubit, R, Mattioli, M, Alvarez, MJ, Shen, Y, Gallahan, D, Singer, D, Saez-Rodriguez, J, Xie, Y, Stolovitzky, G, Califano, A, Abbuehl, JP, Altman, RB, Balcome, S, Bell, A, Bender, A, Berger, B, Bernard, J, Bieberich, AA, Borboudakis, G, Chan, C, Chen, TH, Choi, J, Coelho, LP, Creighton, CJ, Dampier, W, Davisson, VJ, Deshpande, R, Diao, L, Di Camillo, B, Dundar, M, Ertel, A, Goswami, CP, Gottlieb, A, Gould, MN, Goya, J, Grau, M, Gray, JW, Hejase, HA, Hoffmann, MF, Homicsko, K, Homilius, M, Hwang, W, Ijzerman, AP, Kallioniemi, O, Karacali, B, Kaski, S, Kim, J, Krishnan, A, Lee, J, Lee, YS, Lenselink, EB, Lenz, P, Li, L, Li, J, Liang, H, Mpindi, JP, Myers, CL, Newton, MA, Overington, JP, Parkkinen, J, Prill, RJ, Peng, J, Pestell, R, Qiu, P, Rajwa, B, Sadanandam, A, Sambo, F, Sridhar, A, Sun, W, Toffolo, GM, Tozeren, A, Troyanskaya, OG, Tsamardinos, I, Van Vlijmen, HWT, Wang, W, Wegner, JK, Wennerberg, K, Van Westen, GJP, Xia, T, Yang, Y, Yao, V, Yuan, Y, Zeng, H, Zhang, S, Zhao, J & Zhou, J 2014, 'A community computational challenge to predict the activity of pairs of compounds' Nature Biotechnology, vol 32, no. 12, pp. 1213-1222. DOI: 10.1038/nbt.3052
    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. Nature Biotechnology. 2014 Dec 1;32(12):1213-1222. Available from, DOI: 10.1038/nbt.3052

    Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P.; Costello, James C.; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M.; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J.; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea; Abbuehl, Jean Paul; Altman, Russ B.; Balcome, Shawn; Bell, Ana; Bender, Andreas; Berger, Bonnie; Bernard, Jonathan; Bieberich, Andrew A.; Borboudakis, Giorgos; Chan, Christina; Chen, Ting Huei; Choi, Jaejoon; Coelho, Luis Pedro; Creighton, Chad J.; Dampier, Will; Davisson, V. Jo; Deshpande, Raamesh; Diao, Lixia; Di Camillo, Barbara; Dundar, Murat; Ertel, Adam; Goswami, Chirayu P.; Gottlieb, Assaf; Gould, Michael N.; Goya, Jonathan; Grau, Michael; Gray, Joe W.; Hejase, Hussein A.; Hoffmann, Michael F.; Homicsko, Krisztian; Homilius, Max; Hwang, Woochang; Ijzerman, Adriaan P.; Kallioniemi, Olli; Karacali, Bilge; Kaski, Samuel; Kim, Junho; Krishnan, Arjun; Lee, Junehawk; Lee, Young Suk; Lenselink, Eelke B.; Lenz, Peter; Li, Lang; Li, Jun; Liang, Han; Mpindi, John Patrick; Myers, Chad L.; Newton, Michael A.; Overington, John P.; Parkkinen, Juuso; Prill, Robert J.; Peng, Jian; Pestell, Richard; Qiu, Peng; Rajwa, Bartek; Sadanandam, Anguraj; Sambo, Francesco; Sridhar, Arvind; Sun, Wei; Toffolo, Gianna M.; Tozeren, Aydin; Troyanskaya, Olga G.; Tsamardinos, Ioannis; Van Vlijmen, Herman W T; Wang, Wen; Wegner, Joerg K.; Wennerberg, Krister; Van Westen, Gerard J P; Xia, Tian; Yang, Yang; Yao, Victoria; Yuan, Yuan; Zeng, Haoyang; Zhang, Shihua; Zhao, Junfei; Zhou, Jian / A community computational challenge to predict the activity of pairs of compounds.

    In: Nature Biotechnology, Vol. 32, No. 12, 01.12.2014, p. 1213-1222.

    Research output: Contribution to journalArticle

    @article{99aab1857ed14f088125d37478a4a6f3,
    title = "A community computational challenge to predict the activity of pairs of compounds",
    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.",
    author = "Mukesh Bansal and Jichen Yang and Charles Karan and Menden, {Michael P.} and Costello, {James C.} and Hao Tang and Guanghua Xiao and Yajuan Li and Jeffrey Allen and Rui Zhong and Beibei Chen and Minsoo Kim and Tao Wang and Heiser, {Laura M.} and Ronald Realubit and Michela Mattioli and Alvarez, {Mariano J.} and Yao Shen and Daniel Gallahan and Dinah Singer and Julio Saez-Rodriguez and Yang Xie and Gustavo Stolovitzky and Andrea Califano and Abbuehl, {Jean Paul} and Altman, {Russ B.} and Shawn Balcome and Ana Bell and Andreas Bender and Bonnie Berger and Jonathan Bernard and Bieberich, {Andrew A.} and Giorgos Borboudakis and Christina Chan and Chen, {Ting Huei} and Jaejoon Choi and Coelho, {Luis Pedro} and Creighton, {Chad J.} and Will Dampier and Davisson, {V. Jo} and Raamesh Deshpande and Lixia Diao and {Di Camillo}, Barbara and Murat Dundar and Adam Ertel and Goswami, {Chirayu P.} and Assaf Gottlieb and Gould, {Michael N.} and Jonathan Goya and Michael Grau and Gray, {Joe W.} and Hejase, {Hussein A.} and Hoffmann, {Michael F.} and Krisztian Homicsko and Max Homilius and Woochang Hwang and Ijzerman, {Adriaan P.} and Olli Kallioniemi and Bilge Karacali and Samuel Kaski and Junho Kim and Arjun Krishnan and Junehawk Lee and Lee, {Young Suk} and Lenselink, {Eelke B.} and Peter Lenz and Lang Li and Jun Li and Han Liang and Mpindi, {John Patrick} and Myers, {Chad L.} and Newton, {Michael A.} and Overington, {John P.} and Juuso Parkkinen and Prill, {Robert J.} and Jian Peng and Richard Pestell and Peng Qiu and Bartek Rajwa and Anguraj Sadanandam and Francesco Sambo and Arvind Sridhar and Wei Sun and Toffolo, {Gianna M.} and Aydin Tozeren and Troyanskaya, {Olga G.} and Ioannis Tsamardinos and {Van Vlijmen}, {Herman W T} and Wen Wang and Wegner, {Joerg K.} and Krister Wennerberg and {Van Westen}, {Gerard J P} and Tian Xia and Yang Yang and Victoria Yao and Yuan Yuan and Haoyang Zeng and Shihua Zhang and Junfei Zhao and Jian Zhou",
    year = "2014",
    month = "12",
    doi = "10.1038/nbt.3052",
    volume = "32",
    pages = "1213--1222",
    journal = "Nature Biotechnology",
    issn = "1087-0156",
    publisher = "Nature Publishing Group",
    number = "12",

    }

    TY - JOUR

    T1 - A community computational challenge to predict the activity of pairs of compounds

    AU - Bansal,Mukesh

    AU - Yang,Jichen

    AU - Karan,Charles

    AU - Menden,Michael P.

    AU - Costello,James C.

    AU - Tang,Hao

    AU - Xiao,Guanghua

    AU - Li,Yajuan

    AU - Allen,Jeffrey

    AU - Zhong,Rui

    AU - Chen,Beibei

    AU - Kim,Minsoo

    AU - Wang,Tao

    AU - Heiser,Laura M.

    AU - Realubit,Ronald

    AU - Mattioli,Michela

    AU - Alvarez,Mariano J.

    AU - Shen,Yao

    AU - Gallahan,Daniel

    AU - Singer,Dinah

    AU - Saez-Rodriguez,Julio

    AU - Xie,Yang

    AU - Stolovitzky,Gustavo

    AU - Califano,Andrea

    AU - Abbuehl,Jean Paul

    AU - Altman,Russ B.

    AU - Balcome,Shawn

    AU - Bell,Ana

    AU - Bender,Andreas

    AU - Berger,Bonnie

    AU - Bernard,Jonathan

    AU - Bieberich,Andrew A.

    AU - Borboudakis,Giorgos

    AU - Chan,Christina

    AU - Chen,Ting Huei

    AU - Choi,Jaejoon

    AU - Coelho,Luis Pedro

    AU - Creighton,Chad J.

    AU - Dampier,Will

    AU - Davisson,V. Jo

    AU - Deshpande,Raamesh

    AU - Diao,Lixia

    AU - Di Camillo,Barbara

    AU - Dundar,Murat

    AU - Ertel,Adam

    AU - Goswami,Chirayu P.

    AU - Gottlieb,Assaf

    AU - Gould,Michael N.

    AU - Goya,Jonathan

    AU - Grau,Michael

    AU - Gray,Joe W.

    AU - Hejase,Hussein A.

    AU - Hoffmann,Michael F.

    AU - Homicsko,Krisztian

    AU - Homilius,Max

    AU - Hwang,Woochang

    AU - Ijzerman,Adriaan P.

    AU - Kallioniemi,Olli

    AU - Karacali,Bilge

    AU - Kaski,Samuel

    AU - Kim,Junho

    AU - Krishnan,Arjun

    AU - Lee,Junehawk

    AU - Lee,Young Suk

    AU - Lenselink,Eelke B.

    AU - Lenz,Peter

    AU - Li,Lang

    AU - Li,Jun

    AU - Liang,Han

    AU - Mpindi,John Patrick

    AU - Myers,Chad L.

    AU - Newton,Michael A.

    AU - Overington,John P.

    AU - Parkkinen,Juuso

    AU - Prill,Robert J.

    AU - Peng,Jian

    AU - Pestell,Richard

    AU - Qiu,Peng

    AU - Rajwa,Bartek

    AU - Sadanandam,Anguraj

    AU - Sambo,Francesco

    AU - Sridhar,Arvind

    AU - Sun,Wei

    AU - Toffolo,Gianna M.

    AU - Tozeren,Aydin

    AU - Troyanskaya,Olga G.

    AU - Tsamardinos,Ioannis

    AU - Van Vlijmen,Herman W T

    AU - Wang,Wen

    AU - Wegner,Joerg K.

    AU - Wennerberg,Krister

    AU - Van Westen,Gerard J P

    AU - Xia,Tian

    AU - Yang,Yang

    AU - Yao,Victoria

    AU - Yuan,Yuan

    AU - Zeng,Haoyang

    AU - Zhang,Shihua

    AU - Zhao,Junfei

    AU - Zhou,Jian

    PY - 2014/12/1

    Y1 - 2014/12/1

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=84924338899&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84924338899&partnerID=8YFLogxK

    U2 - 10.1038/nbt.3052

    DO - 10.1038/nbt.3052

    M3 - Article

    VL - 32

    SP - 1213

    EP - 1222

    JO - Nature Biotechnology

    T2 - Nature Biotechnology

    JF - Nature Biotechnology

    SN - 1087-0156

    IS - 12

    ER -