Inferring causal molecular networks: empirical assessment through a community-based effort
Creators: name: Carlin, Daniel E.
name: Hill, Steven M.
name: Meerzaman, Daoud
name: Kannan, Venkateshan
name: Afsari, Bahman
name: Hase, Takeshi
name: Budak, Gungor
name: Lee, Wai Shing
name: Caglar, Mehmet
name: Stuart, Joshua M.
name: Coort, Susan
name: Haider, Saad
name: Friend, Stephen
name: Carlon, Azzurra
name: Zairis, Sakellarios
name: Cai, Binghuang
name: Sichani, Omid Askari
name: Komatsoulis, George
name: Sambo, Francesco
name: Kursa, Miron Bartosz
name: Kikuchi, Kaito
name: Nesser, Nicole K.
name: Anton, Bernat
name: Wang, Haizhou
name: Huang, Xun
name: Bonneau, Richard
name: Knapp, Bettina
name: Berlow, Noah
name: Wan, Qian
name: Graim, Kiley
name: Paull, Evan O.
name: Guan, Yuanfang
name: Gao, Xi
name: Lu, Songjian
name: Trifoglio, Emanuele
name: Neapolitan, Richard E.
name: Hafemeister, Christoph
name: Finotello, Francesca
name: Linger, Michael
name: Bonet, Jaume
name: Saez-Rodriguez, Julio
name: Zhang, Yang
name: Zi, Zhike
name: Min, Wenwen
name: Al-Ouran, Rami
name: Giaretta, Alberto
name: Strunz, Sonja
name: Bagheri, Neda
name: Di Camillo, Barbara
name: Bohler, Anwesha
name: Hu, Ying
name: Creighton, Chad J.
name: Poglayen, Daniel
name: Song, Mingzhou
name: Ghosh, Samik
name: Kaderali, Lars
name: Arodz, Tomasz
name: Evelo, Chris
name: Bivol, Adrian
name: Kitano, Hiroaki
name: Zengerling, Michael
name: Qutub, Amina A.
name: Pal, Ranadip
name: Sanavia, Tiziana
name: Xue, Albert Y.
name: Liu, Yu
name: Cokelaer, Thomas
name: Gray, Joe W.
name: Mills, Gordon B.
name: Fertig, Elana J.
name: Palinkas, Aljoscha
name: Tegnér, Jesper
name: Li, Yichao
name: Chen, Lujia
name: Mukherjee, Sach
name: Emmett, Kevin
name: Hodgson, Jay
name: Jiang, Xia
name: Oliva, Baldo
name: Yamanaka, Ryota
name: Yan, Chunhua
name: Spellman, Paul T.
name: Welch, Lonnie
name: Großeholz, Ruth
name: Kellen, Michael
name: Sharifi-Zarchi, Ali
name: Ciaccio, Mark F.
name: Guinney, Justin
name: Thobe, Kirste
name: Norman, Thea
name: Zenil, Hector
name: Hu, Chenyue W.
name: Krämer, Andreas
name: Cooper, Gregory
name: Taylor, Dane
name: Bisberg, Alexander J.
name: Long, Byron L.
name: Streck, Adam
name: Kacprowski, Tim
name: Manfrini, Marco
name: Sokolov, Artem
name: Jalili, Mahdi
name: Bunescu, Razvan
name: Liang, Xiaoyu
name: Kang, Mingon
name: Müller, Christian Lorenz
name: Heiser, Laura M.
name: Zhu, Fan
name: Hoff, Bruce
name: Kutmon, Martina
name: Noren, David P.
name: Dutta-Moscato, Joyeeta
name: Wong, Chris K.
name: Lu, Xinghua
name: Favorov, Alexander V.
name: Hahn, Oliver
name: Finkle, Justin D.
name: Planas-Iglesias, Joan
name: Liu, Zhaoqi
name: Fassia, Mohammad-Kasim H.
name: Stolovitzky, Gustavo
name: Daneshmand, Seyed-Mohammad-Hadi
name: Unger, Michael
name: Cai, Chunhui
name: Koeppl, Heinz
name: Matos, Marta R. A.
name: Kim, Dong-Chul
name: Gao, Jean
name: Hsu, Chih Hao
name: Danilova, Ludmila V.
name: Toffolo, Gianna Maria
name: Wu, Jia J.
name: De La Fuente, Alberto
name: Slawek, Janusz
name: Opiyo, Stephen Obol
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that 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 constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and 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 the causal validity of inferred molecular networks.