Hill, Steven M. ; Heiser, Laura M. ; Cokelaer, Thomas ; Unger, 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 (2016)
Inferring causal molecular networks: empirical assessment through a community-based effort.
In: Nature Methods, 13 (4)
doi: 10.1038/nmeth.3773
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Hill, Steven M. ; Heiser, Laura M. ; Cokelaer, Thomas ; Unger, 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 |
| Art des Eintrags: | Bibliographie |
| Titel: | Inferring causal molecular networks: empirical assessment through a community-based effort |
| Sprache: | Englisch |
| Publikationsjahr: | Februar 2016 |
| Ort: | London |
| Verlag: | Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Nature Methods |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 4 |
| DOI: | 10.1038/nmeth.3773 |
| URL / URN: | https://www.nature.com/articles/nmeth.3773 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab |
| Hinterlegungsdatum: | 02 Mai 2024 12:16 |
| Letzte Änderung: | 09 Aug 2024 06:38 |
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| Suche nach Titel in: | TUfind oder in Google |
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Inferring causal molecular networks: empirical assessment through a community-based effort. (deposited 22 Apr 2024 09:53)
- Inferring causal molecular networks: empirical assessment through a community-based effort. (deposited 02 Mai 2024 12:16) [Gegenwärtig angezeigt]
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