Inferring causal molecular networks: Empirical assessment through a community-based effort

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dc.creatorHill, Steven M.
dc.creatorHeiser, Laura M.
dc.creatorCokelaer, Thomas
dc.creatorLinger, Michael
dc.creatorNesser, Nicole K.
dc.creatorCarlin, Daniel E.
dc.creatorZhang, Yang
dc.creatorSokolov, Artem
dc.creatorPaull, Evan O.
dc.creatorWong, Chris K.
dc.creatorGraim, Kiley
dc.creatorBivol, Adrian
dc.creatorWang, Haizhou
dc.creatorZhu, Fan
dc.creatorAfsari, Bahman
dc.creatorDanilova, Ludmila V.
dc.creatorFavorov, Alexander V.
dc.creatorLee, Wai Shing
dc.creatorTaylor, Dane
dc.creatorHu, Chenyue W.
dc.creatorLong, Byron L.
dc.creatorNoren, David P.
dc.creatorBisberg, Alexander J.
dc.creatorMills, Gordon B.
dc.creatorGray, Joe W.
dc.creatorKellen, Michael
dc.creatorNorman, Thea
dc.creatorFriend, Stephen
dc.creatorQutub, Amina A.
dc.creatorFertig, Elana J.
dc.creatorGuan, Yuanfang
dc.creatorSong, Mingzhou
dc.creatorStuart, Joshua M.
dc.creatorSpellman, Paul T.
dc.creatorKoeppl, Heinz
dc.creatorStolovitzky, Gustavo
dc.creatorSaez-Rodriguez, Julio
dc.creatorMukherjee, Sach
dc.creatorAl-Ouran, Rami
dc.creatorAnton, Bernat
dc.creatorArodz, Tomasz
dc.creatorSichani, Omid Askari
dc.creatorBagheri, Neda
dc.creatorBerlow, Noah (TTU)
dc.creatorBohler, Anwesha
dc.creatorBonet, Jaume
dc.creatorBonneau, Richard
dc.creatorBudak, Gungor
dc.creatorBunescu, Razvan
dc.creatorCaglar, Mehmet (TTU)
dc.creatorCai, Binghuang
dc.creatorCai, Chunhui
dc.creatorCarlon, Azzurra
dc.creatorChen, Lujia
dc.creatorCiaccio, Mark F.
dc.creatorCooper, Gregory
dc.creatorCoort, Susan
dc.creatorCreighton, Chad J.
dc.creatorDaneshmand, Seyed Mohammad Hadi
dc.creatorDe La Fuente, Alberto
dc.creatorDi Camillo, Barbara
dc.creatorDutta-Moscato, Joyeeta
dc.creatorEmmett, Kevin
dc.creatorEvelo, Chris
dc.creatorFassia, Mohammad Kasim H.
dc.creatorFinotello, Francesca
dc.creatorFinkle, Justin D.
dc.creatorGao, Xi
dc.creatorGao, Jean
dc.creatorGhosh, Samik
dc.creatorGiaretta, Alberto
dc.creatorGroßeholz, Ruth
dc.creatorGuinney, Justin
dc.creatorHafemeister, Christoph
dc.creatorHahn, Oliver
dc.creatorHaider, Saad (TTU)
dc.creatorHase, Takeshi
dc.creatorHodgson, Jay
dc.creatorHoff, Bruce
dc.creatorHsu, Chih Hao
dc.creatorHu, Ying
dc.creatorHuang, Xun
dc.creatorJalili, Mahdi
dc.creatorJiang, Xia
dc.creatorKacprowski, Tim
dc.creatorKaderali, Lars
dc.creatorKang, Mingon
dc.creatorKannan, Venkateshan
dc.creatorKikuchi, Kaito
dc.creatorKim, Dong Chul
dc.creatorKitano, Hiroaki
dc.creatorKnapp, Bettina
dc.creatorKomatsoulis, George
dc.creatorKrämer, Andreas
dc.creatorKursa, Miron Bartosz
dc.creatorKutmon, Martina
dc.creatorLi, Yichao
dc.creatorLiang, Xiaoyu
dc.creatorLiu, Zhaoqi
dc.creatorLiu, Yu
dc.creatorLu, Songjian
dc.creatorLu, Xinghua
dc.creatorManfrini, Marco
dc.creatorMatos, Marta R.A.
dc.creatorMeerzaman, Daoud
dc.creatorMin, Wenwen
dc.creatorMüller, Christian Lorenz
dc.creatorNeapolitan, Richard E.
dc.creatorOliva, Baldo
dc.creatorOpiyo, Stephen Obol
dc.creatorPal, Ranadip (TTU)
dc.creatorPalinkas, Aljoscha
dc.creatorPlanas-Iglesias, Joan
dc.creatorPoglayen, Daniel
dc.creatorSambo, Francesco
dc.creatorSanavia, Tiziana
dc.creatorSharifi-Zarchi, Ali
dc.creatorSlawek, Janusz
dc.creatorStreck, Adam
dc.creatorStrunz, Sonja
dc.creatorTegnér, Jesper
dc.creatorThobe, Kirste
dc.creatorToffolo, Gianna Maria
dc.creatorTrifoglio, Emanuele
dc.creatorUnger, Michael
dc.creatorWan, Qian (TTU)
dc.creatorWelch, Lonnie
dc.creatorWu, Jia J.
dc.creatorXue, Albert Y.
dc.creatorYamanaka, Ryota
dc.creatorYan, Chunhua
dc.creatorZairis, Sakellarios
dc.creatorZengerling, Michael
dc.creatorZenil, Hector
dc.creatorZi, Zhike
dc.date.accessioned2023-05-24T18:41:22Z
dc.date.available2023-05-24T18:41:22Z
dc.date.issued2016
dc.description© 2016 Nature America, Inc. All rights reserved. cc-by-nc-sa
dc.description.abstractIt remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
dc.identifier.citationHill, S.M., Heiser, L.M., Cokelaer, T., Linger, M., Nesser, N.K., Carlin, D.E., Zhang, Y., Sokolov, A., Paull, E.O., Wong, C.K., Graim, K., Bivol, A., Wang, H., Zhu, F., Afsari, B., Danilova, L.V., Favorov, A.V., Lee, W.S., Taylor, D., . . . Zi, Z.. 2016. Inferring causal molecular networks: Empirical assessment through a community-based effort. Nature Methods, 13(4). https://doi.org/10.1038/nmeth.3773
dc.identifier.urihttps://doi.org/10.1038/nmeth.3773
dc.identifier.urihttps://hdl.handle.net/2346/93665
dc.language.isoeng
dc.titleInferring causal molecular networks: Empirical assessment through a community-based effort
dc.typeArticle

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