Abstract
It 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.
Original language | English (US) |
---|---|
Pages (from-to) | 310-322 |
Number of pages | 13 |
Journal | Nature Methods |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - Mar 30 2016 |
ASJC Scopus subject areas
- Biotechnology
- Biochemistry
- Molecular Biology
- Cell Biology
Access to Document
Other files and links
Fingerprint
Dive into the research topics of 'Inferring causal molecular networks: Empirical assessment through a community-based effort'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
Inferring causal molecular networks : Empirical assessment through a community-based effort. / Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Linger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach; Al-Ouran, Rami; Anton, Bernat; Arodz, Tomasz; Sichani, Omid Askari; Bagheri, Neda; Berlow, Noah; Bohler, Anwesha; Bonet, Jaume; Bonneau, Richard; Budak, Gungor; Bunescu, Razvan; Caglar, Mehmet; Cai, Binghuang; Cai, Chunhui; Carlon, Azzurra; Chen, Lujia; Ciaccio, Mark F.; Cooper, Gregory; Coort, Susan; Creighton, Chad J.; Daneshmand, Seyed Mohammad Hadi; De La Fuente, Alberto; Di Camillo, Barbara; Dutta-Moscato, Joyeeta; Emmett, Kevin; Evelo, Chris; Fassia, Mohammad Kasim H.; Finotello, Francesca; Finkle, Justin D.; Gao, Xi; Garcia-Garcia, Javier; Ghosh, Samik; Giaretta, Alberto; Großeholz, Ruth; Guinney, Justin; Hafemeister, Christoph; Hahn, Oliver; Haider, Saad; Hase, Takeshi; Hodgson, Jay; Hoff, Bruce; Hsu, Chih Hao; Hu, Ying; Huang, Xun; Jalili, Mahdi; Jiang, Xia; Kacprowski, Tim; Kaderali, Lars; Kang, Mingon; Kannan, Venkateshan; Kikuchi, Kaito; Kim, Dong Chul; Kitano, Hiroaki; Knapp, Bettina; Komatsoulis, George; Krämer, Andreas; Kursa, Miron Bartosz; Kutmon, Martina; Li, Yichao; Liang, Xiaoyu; Liu, Zhaoqi; Liu, Yu; Lu, Songjian; Lu, Xinghua; Manfrini, Marco; Matos, Marta R.A.; Meerzaman, Daoud; Min, Wenwen; Müller, Christian Lorenz; Neapolitan, Richard E.; Oliva, Baldo; Opiyo, Stephen Obol; Pal, Ranadip; Palinkas, Aljoscha; Planas-Iglesias, Joan; Poglayen, Daniel; Sambo, Francesco; Sanavia, Tiziana; Sharifi-Zarchi, Ali; Slawek, Janusz; Streck, Adam; Strunz, Sonja; Tegnér, Jesper; Thobe, Kirste; Toffolo, Gianna Maria; Trifoglio, Emanuele; Unger, Michael; Wan, Qian; Welch, Lonnie; Wu, Jia J.; Xue, Albert Y.; Yamanaka, Ryota; Yan, Chunhua; Zairis, Sakellarios; Zengerling, Michael; Zenil, Hector; Zi, Zhike.
In: Nature Methods, Vol. 13, No. 4, 30.03.2016, p. 310-322.Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Inferring causal molecular networks
T2 - Empirical assessment through a community-based effort
AU - Hill, Steven M.
AU - Heiser, Laura M.
AU - Cokelaer, Thomas
AU - Linger, Michael
AU - Nesser, Nicole K.
AU - Carlin, Daniel E.
AU - Zhang, Yang
AU - Sokolov, Artem
AU - Paull, Evan O.
AU - Wong, Chris K.
AU - Graim, Kiley
AU - Bivol, Adrian
AU - Wang, Haizhou
AU - Zhu, Fan
AU - Afsari, Bahman
AU - Danilova, Ludmila V.
AU - Favorov, Alexander V.
AU - Lee, Wai Shing
AU - Taylor, Dane
AU - Hu, Chenyue W.
AU - Long, Byron L.
AU - Noren, David P.
AU - Bisberg, Alexander J.
AU - Mills, Gordon B.
AU - Gray, Joe W.
AU - Kellen, Michael
AU - Norman, Thea
AU - Friend, Stephen
AU - Qutub, Amina A.
AU - Fertig, Elana J.
AU - Guan, Yuanfang
AU - Song, Mingzhou
AU - Stuart, Joshua M.
AU - Spellman, Paul T.
AU - Koeppl, Heinz
AU - Stolovitzky, Gustavo
AU - Saez-Rodriguez, Julio
AU - Mukherjee, Sach
AU - Al-Ouran, Rami
AU - Anton, Bernat
AU - Arodz, Tomasz
AU - Sichani, Omid Askari
AU - Bagheri, Neda
AU - Berlow, Noah
AU - Bohler, Anwesha
AU - Bonet, Jaume
AU - Bonneau, Richard
AU - Budak, Gungor
AU - Bunescu, Razvan
AU - Caglar, Mehmet
AU - Cai, Binghuang
AU - Cai, Chunhui
AU - Carlon, Azzurra
AU - Chen, Lujia
AU - Ciaccio, Mark F.
AU - Cooper, Gregory
AU - Coort, Susan
AU - Creighton, Chad J.
AU - Daneshmand, Seyed Mohammad Hadi
AU - De La Fuente, Alberto
AU - Di Camillo, Barbara
AU - Dutta-Moscato, Joyeeta
AU - Emmett, Kevin
AU - Evelo, Chris
AU - Fassia, Mohammad Kasim H.
AU - Finotello, Francesca
AU - Finkle, Justin D.
AU - Gao, Xi
AU - Garcia-Garcia, Javier
AU - Ghosh, Samik
AU - Giaretta, Alberto
AU - Großeholz, Ruth
AU - Guinney, Justin
AU - Hafemeister, Christoph
AU - Hahn, Oliver
AU - Haider, Saad
AU - Hase, Takeshi
AU - Hodgson, Jay
AU - Hoff, Bruce
AU - Hsu, Chih Hao
AU - Hu, Ying
AU - Huang, Xun
AU - Jalili, Mahdi
AU - Jiang, Xia
AU - Kacprowski, Tim
AU - Kaderali, Lars
AU - Kang, Mingon
AU - Kannan, Venkateshan
AU - Kikuchi, Kaito
AU - Kim, Dong Chul
AU - Kitano, Hiroaki
AU - Knapp, Bettina
AU - Komatsoulis, George
AU - Krämer, Andreas
AU - Kursa, Miron Bartosz
AU - Kutmon, Martina
AU - Li, Yichao
AU - Liang, Xiaoyu
AU - Liu, Zhaoqi
AU - Liu, Yu
AU - Lu, Songjian
AU - Lu, Xinghua
AU - Manfrini, Marco
AU - Matos, Marta R.A.
AU - Meerzaman, Daoud
AU - Min, Wenwen
AU - Müller, Christian Lorenz
AU - Neapolitan, Richard E.
AU - Oliva, Baldo
AU - Opiyo, Stephen Obol
AU - Pal, Ranadip
AU - Palinkas, Aljoscha
AU - Planas-Iglesias, Joan
AU - Poglayen, Daniel
AU - Sambo, Francesco
AU - Sanavia, Tiziana
AU - Sharifi-Zarchi, Ali
AU - Slawek, Janusz
AU - Streck, Adam
AU - Strunz, Sonja
AU - Tegnér, Jesper
AU - Thobe, Kirste
AU - Toffolo, Gianna Maria
AU - Trifoglio, Emanuele
AU - Unger, Michael
AU - Wan, Qian
AU - Welch, Lonnie
AU - Wu, Jia J.
AU - Xue, Albert Y.
AU - Yamanaka, Ryota
AU - Yan, Chunhua
AU - Zairis, Sakellarios
AU - Zengerling, Michael
AU - Zenil, Hector
AU - Zi, Zhike
N1 - Publisher Copyright: © 2016 Nature America, Inc. All rights reserved.
PY - 2016/3/30
Y1 - 2016/3/30
N2 - It 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.
AB - It 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.
UR - http://www.scopus.com/inward/record.url?scp=84978621488&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978621488&partnerID=8YFLogxK
U2 - 10.1038/nmeth.3773
DO - 10.1038/nmeth.3773
M3 - Article
C2 - 26901648
AN - SCOPUS:84978621488
VL - 13
SP - 310
EP - 322
JO - Nature Methods
JF - Nature Methods
SN - 1548-7091
IS - 4
ER -