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

  1. Hill, Steven M.
  2. Heiser, Laura M.
  3. Cokelaer, Thomas
  4. Unger, Michael
  5. Nesser, Nicole K.
  6. Carlin, Daniel E.
  7. Zhang, Yang
  8. Sokolov, Artem
  9. Paull, Evan O.
  10. Wong, Chris K.
  11. Graim, Kiley
  12. Bivol, Adrian
  13. Wang, Haizhou
  14. Zhu, Fan
  15. Afsari, Bahman
  16. Danilova, Ludmila V.
  17. Favorov, Alexander V.
  18. Lee, Wai Shing
  19. Taylor, Dane
  20. Hu, Chenyue W.
  21. Long, Byron L.
  22. Noren, David P.
  23. Bisberg, Alexander J.
  24. Mills, Gordon B.
  25. Gray, Joe W.
  26. Kellen, Michael
  27. Norman, Thea
  28. Friend, Stephen
  29. Qutub, Amina A.
  30. Fertig, Elana J.
  31. Guan, Yuanfang
  32. Song, Mingzhou
  33. Stuart, Joshua M.
  34. Spellman, Paul T.
  35. Koeppl, Heinz
  36. Stolovitzky, Gustavo
  37. Saez-Rodriguez, Julio
  38. Mukherjee, Sach
  39. Guinney, Justin
  40. Hodgson, Jay
  41. Hoff, Bruce
  42. Al-Ouran, Rami
  43. Bunescu, Razvan
  44. Li, Yichao
  45. Liang, Xiaoyu
  46. Welch, Lonnie
  47. Anton, Bernat
  48. Bonet, Jaume
  49. Garcia-Garcia, Javier
  50. Oliva, Baldo
  51. Planas-Iglesias, Joan
  52. Poglayen, Daniel
  53. Arodz, Tomasz
  54. Gao, Xi
  55. Slawek, Janusz
  56. Askari Sichani, Omid
  57. Daneshmand, Seyed-Mohammad-Hadi
  58. Jalili, Mahdi
  59. Bagheri, Neda
  60. Ciaccio, Mark F.
  61. Xue, Albert Y.
  62. Berlow, Noah
  63. Haider, Saad
  64. Pal, Ranadip
  65. Wan, Qian
  66. Bohler, Anwesha
  67. Budak, Gungor
  68. Evelo, Chris
  69. Kutmon, Martina
  70. Bonneau, Richard
  71. Hafemeister, Christoph
  72. Müller, Christian Lorenz
  73. Caglar, Mehmet
  74. Cai, Binghuang
  75. Cai, Chunhui
  76. Chen, Luija
  77. Cooper, Gregory
  78. Dutta-Moscato, Joyeeta
  79. Jiang, Xia
  80. Lu, Songjian
  81. Lu, Xinghua
  82. Carlon, Azzurra
  83. Di Camillo, Barbara
  84. Finotello, Francesca
  85. Giaretta, Alberto
  86. Manfrini, Marco
  87. Sambo, Francesco
  88. Sanavia, Tiziana
  89. Toffolo, Gianna Maria
  90. Trifoglio, Emanuele
  91. Creighton, Chad J.
  92. de la Fuente, Alberto
  93. Strunz, Sonja
  94. Emmett, Kevin
  95. Fassia, Mohammad-Kasim H.
  96. Finkle, Justin D.
  97. Wu, Jia J.
  98. Gao, Jean
  99. Kang, Mingon
  100. Ghosh, Samik
  101. Hase, Takeshi
  102. Kikuchi, Kaito
  103. Kitano, Hiroaki
  104. Yamanaka, Ryota
  105. Großeholz, Ruth
  106. Hahn, Oliver
  107. Zengerling, Michael
  108. Hsu, Chih Hao
  109. Hu, Ying
  110. Komatsoulis, George
  111. Meerzaman, Daoud
  112. Yan, Chunhua
  113. Huang, Xun
  114. Zi, Zhike
  115. Kacprowski, Tim
  116. Kaderali, Lars
  117. Knapp, Bettina
  118. Matos, Marta R. A.
  119. Kannan, Venkateshan
  120. Tegnér, Jesper
  121. Zenil, Hector
  122. Kim, Dong-Chul
  123. Krämer, Andreas
  124. Kursa, Miron Bartosz
  125. Liu, Zhaoqi
  126. Min, Wenwen
  127. Zhang, Shihua
  128. Liu, Yu
  129. Neapolitan, Richard E.
  130. Obol Opiyo, Stephen
  131. Palinkas, Aljoscha
  132. Streck, Adam
  133. Thobe, Kirste
  134. Sharifi-Zarchi, Ali
  135. Zairis, Sakellarios

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1000 Titel
  • Inferring causal molecular networks: empirical assessment through a community-based effort
1000 Autor/in
  1. Hill, Steven M. |
  2. Heiser, Laura M. |
  3. Cokelaer, Thomas |
  4. Unger, Michael |
  5. Nesser, Nicole K. |
  6. Carlin, Daniel E. |
  7. Zhang, Yang |
  8. Sokolov, Artem |
  9. Paull, Evan O. |
  10. Wong, Chris K. |
  11. Graim, Kiley |
  12. Bivol, Adrian |
  13. Wang, Haizhou |
  14. Zhu, Fan |
  15. Afsari, Bahman |
  16. Danilova, Ludmila V. |
  17. Favorov, Alexander V. |
  18. Lee, Wai Shing |
  19. Taylor, Dane |
  20. Hu, Chenyue W. |
  21. Long, Byron L. |
  22. Noren, David P. |
  23. Bisberg, Alexander J. |
  24. Mills, Gordon B. |
  25. Gray, Joe W. |
  26. Kellen, Michael |
  27. Norman, Thea |
  28. Friend, Stephen |
  29. Qutub, Amina A. |
  30. Fertig, Elana J. |
  31. Guan, Yuanfang |
  32. Song, Mingzhou |
  33. Stuart, Joshua M. |
  34. Spellman, Paul T. |
  35. Koeppl, Heinz |
  36. Stolovitzky, Gustavo |
  37. Saez-Rodriguez, Julio |
  38. Mukherjee, Sach |
  39. Guinney, Justin |
  40. Hodgson, Jay |
  41. Hoff, Bruce |
  42. Al-Ouran, Rami |
  43. Bunescu, Razvan |
  44. Li, Yichao |
  45. Liang, Xiaoyu |
  46. Welch, Lonnie |
  47. Anton, Bernat |
  48. Bonet, Jaume |
  49. Garcia-Garcia, Javier |
  50. Oliva, Baldo |
  51. Planas-Iglesias, Joan |
  52. Poglayen, Daniel |
  53. Arodz, Tomasz |
  54. Gao, Xi |
  55. Slawek, Janusz |
  56. Askari Sichani, Omid |
  57. Daneshmand, Seyed-Mohammad-Hadi |
  58. Jalili, Mahdi |
  59. Bagheri, Neda |
  60. Ciaccio, Mark F. |
  61. Xue, Albert Y. |
  62. Berlow, Noah |
  63. Haider, Saad |
  64. Pal, Ranadip |
  65. Wan, Qian |
  66. Bohler, Anwesha |
  67. Budak, Gungor |
  68. Evelo, Chris |
  69. Kutmon, Martina |
  70. Bonneau, Richard |
  71. Hafemeister, Christoph |
  72. Müller, Christian Lorenz |
  73. Caglar, Mehmet |
  74. Cai, Binghuang |
  75. Cai, Chunhui |
  76. Chen, Luija |
  77. Cooper, Gregory |
  78. Dutta-Moscato, Joyeeta |
  79. Jiang, Xia |
  80. Lu, Songjian |
  81. Lu, Xinghua |
  82. Carlon, Azzurra |
  83. Di Camillo, Barbara |
  84. Finotello, Francesca |
  85. Giaretta, Alberto |
  86. Manfrini, Marco |
  87. Sambo, Francesco |
  88. Sanavia, Tiziana |
  89. Toffolo, Gianna Maria |
  90. Trifoglio, Emanuele |
  91. Creighton, Chad J. |
  92. de la Fuente, Alberto |
  93. Strunz, Sonja |
  94. Emmett, Kevin |
  95. Fassia, Mohammad-Kasim H. |
  96. Finkle, Justin D. |
  97. Wu, Jia J. |
  98. Gao, Jean |
  99. Kang, Mingon |
  100. Ghosh, Samik |
  101. Hase, Takeshi |
  102. Kikuchi, Kaito |
  103. Kitano, Hiroaki |
  104. Yamanaka, Ryota |
  105. Großeholz, Ruth |
  106. Hahn, Oliver |
  107. Zengerling, Michael |
  108. Hsu, Chih Hao |
  109. Hu, Ying |
  110. Komatsoulis, George |
  111. Meerzaman, Daoud |
  112. Yan, Chunhua |
  113. Huang, Xun |
  114. Zi, Zhike |
  115. Kacprowski, Tim |
  116. Kaderali, Lars |
  117. Knapp, Bettina |
  118. Matos, Marta R. A. |
  119. Kannan, Venkateshan |
  120. Tegnér, Jesper |
  121. Zenil, Hector |
  122. Kim, Dong-Chul |
  123. Krämer, Andreas |
  124. Kursa, Miron Bartosz |
  125. Liu, Zhaoqi |
  126. Min, Wenwen |
  127. Zhang, Shihua |
  128. Liu, Yu |
  129. Neapolitan, Richard E. |
  130. Obol Opiyo, Stephen |
  131. Palinkas, Aljoscha |
  132. Streck, Adam |
  133. Thobe, Kirste |
  134. Sharifi-Zarchi, Ali |
  135. Zairis, Sakellarios |
1000 Erscheinungsjahr 2016
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2016-02-22
1000 Erschienen in
1000 Quellenangabe
  • 13(4): 310-318
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2016
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/nmeth.3773 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854847/ |
1000 Ergänzendes Material
  • http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3773.html?foxtrotcallback=true#supplementary-information |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal Cancer models
lokal Cellular signalling networks
lokal Systems biology
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. US National Institutes of Health |
  2. National Cancer Institute (NCI) |
  3. National Institute of General Medical Sciences |
  4. Susan G. Komen Foundation |
  5. Prospect Creek Foundation |
  6. Spanish Ministry of Science and InnovaXon (MICINN) |
  7. European Commission |
  8. - |
  9. Royal Society |
  10. German Federal Ministry of Education and Research (BMBF) |
  11. US National Library of Medicine |
1000 Fördernummer
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  7. -
  8. -
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1000 Förderprogramm
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  9. Wolfson Research Merit Award
  10. GANI_MED Consortium
  11. -
1000 Dateien
1000 Förderung
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    1000 Förderprogramm -
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    1000 Förderprogramm award
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    1000 Förderprogramm -
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    1000 Förderprogramm -
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  7. 1000 joinedFunding-child
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    1000 Förderprogramm FP7 (SHIPREC)
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  8. 1000 joinedFunding-child
    1000 Förderer - |
    1000 Förderprogramm EU ERA-NET Plus Scheme
    1000 Fördernummer -
  9. 1000 joinedFunding-child
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1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6404384.rdf
1000 Erstellt am 2017-09-11T13:20:13.505+0200
1000 Erstellt von 218
1000 beschreibt frl:6404384
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Fri Jun 03 18:32:59 CEST 2022
1000 Objekt bearb. Fri Jun 03 18:32:59 CEST 2022
1000 Vgl. frl:6404384
1000 Oai Id
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1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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