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Stratification of Amyotrophic Lateral Sclerosis Patients: A Crowdsourcing Approach Publisher Pubmed



Kueffner R1 ; Zach N2 ; Bronfeld M3 ; Norel R4 ; Atassi N5 ; Balagurusamy V4 ; Di Camillo B6 ; Chio A7 ; Cudkowicz M5 ; Dillenberger D4 ; Garciagarcia J8 ; Hardiman O9 ; Hoff B10 ; Knight J4 Show All Authors
Authors
  1. Kueffner R1
  2. Zach N2
  3. Bronfeld M3
  4. Norel R4
  5. Atassi N5
  6. Balagurusamy V4
  7. Di Camillo B6
  8. Chio A7
  9. Cudkowicz M5
  10. Dillenberger D4
  11. Garciagarcia J8
  12. Hardiman O9
  13. Hoff B10
  14. Knight J4
  15. Leitner ML11
  16. Li G12
  17. Mangravite L10
  18. Norman T10
  19. Wang L13
  20. Xiao J14
  21. Fang WC15
  22. Peng J14
  23. Yang C16
  24. Chang HJ17
  25. Stolovitzky G4
  26. Alkallas R18
  27. Anghel C19
  28. Avril J20
  29. Bacardit J21
  30. Balser B22
  31. Balser J22
  32. Barsinai Y23
  33. Bendavid N24
  34. Benzion E25
  35. Bliss R22
  36. Cai J22
  37. Chernyshev A26
  38. Chiang JH27
  39. Chicco D28
  40. Corriveau BAN22
  41. Dai J29
  42. Deshpande Y30
  43. Desplats E22
  44. Durgin JS31
  45. Espiritu SMG19
  46. Fan F19
  47. Fevrier P32
  48. Fridley BL33
  49. Godzik A34
  50. Golinska A35
  51. Gordon J36
  52. Graw S29
  53. Guo Y37
  54. Herpelinck T38
  55. Hopkins J19
  56. Huang B19
  57. Jacobsen J39
  58. Jahandideh S40
  59. Jeon J19
  60. Ji W41
  61. Jung K42
  62. Karanevich A29
  63. Koestler DC29
  64. Kozak M43
  65. Kurz C44
  66. Lalansingh C19
  67. Larrieu T20
  68. Lazzarini N21
  69. Lerner B25
  70. Lesinski W45
  71. Liang X46
  72. Lin X19
  73. Lowe J22
  74. Mackey L47
  75. Meier R29
  76. Min W48
  77. Mnich K49
  78. Nahmias V20
  79. Noelmacdonnell J50
  80. Odonnell A22
  81. Paadre S22
  82. Park J51
  83. Polewkoklim A35
  84. Raghavan R29
  85. Rudnicki W35, 49, 52
  86. Saghapour E53
  87. Salomond JB54, 55
  88. Sankaran K56
  89. Sendorek D19
  90. Sharan V57
  91. Shiah YJ19
  92. Sirois JK22
  93. Sumanaweera DN58
  94. Usset J29
  95. Vang YS59
  96. Vens C38
  97. Wadden D60
  98. Wang D19
  99. Wong WC61
  100. Xie X59, 62
  101. Xu Z22
  102. Yang HT27
  103. Yu X63
  104. Zhang H64
  105. Zhang L22
  106. Zhang S41
  107. Zhu S46, 65
Show Affiliations
Authors Affiliations
  1. 1. Icahn School of Medicine at Mount Sinai, New York, NY, United States
  2. 2. Teva Pharmaceuticals, Netanyah, Israel
  3. 3. Prize4Life, Haifa, Israel
  4. 4. IBM Research, Yorktown Heights, NY, United States
  5. 5. Massachusetts General Hospital, Boston, MA, United States
  6. 6. Information Engineering Department, University of Padova, Padova, Italy
  7. 7. University of Turin, Turin, Italy
  8. 8. Pompeu Fabra University, Barcelona, Spain
  9. 9. Institute of Neuroscience, Trinity College, Dublin, Ireland
  10. 10. Sage Bionetworks, Seattle, WA, United States
  11. 11. Accelerating NeuroVentures, Boston, MA, United States
  12. 12. Amazon, Seattle, WA, United States
  13. 13. Zillow, Seattle, WA, United States
  14. 14. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States
  15. 15. Department of Information and Learning Technology, National University, Tainan City, Taiwan
  16. 16. Department of Computer Science and Information Engineering, National University, Tainan City, Taiwan
  17. 17. Faculty of Information Technology, Monash University, Clayton, Australia
  18. 18. Department of Human Genetics, McGill University, Montreal, Canada
  19. 19. Ontario Institute for Cancer Research (OICR), Toronto, Canada
  20. 20. Departement d’Economie Ecole Polytechnique, Paris, France
  21. 21. Interdisciplinary Computing and Complex BioSystems (ICOS) research group, Newcastle University, Tyne, United Kingdom
  22. 22. Veristat Inc, Southborough, MA, United States
  23. 23. Medical Research, Kfar Malal, Israel
  24. 24. Department of Computer Science, Ben-Gurion University of the Negev, Beersheba, Israel
  25. 25. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Negev, Israel
  26. 26. Analytica Laboratories, Hamilton, New Zealand
  27. 27. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City, Taiwan
  28. 28. Princess Margaret Cancer Centre, Toronto, ON, Canada
  29. 29. Department of Biostatistics, University of Kansas, Medical Center, Kansas City, KS, United States
  30. 30. MIT, Department of Mathematics, Cambridge, MA, United States
  31. 31. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
  32. 32. Centre de Recherche en Economie et Statistique (CREST), Paris, France
  33. 33. Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States
  34. 34. Program on Bioinformatics and Systems Biology, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
  35. 35. Institute of Informatics, University of Bialystok, Ciolkowskiego, Bialystok, Poland
  36. 36. Department of Engineering, University of Cambridge, Cambridge, United Kingdom
  37. 37. RTI International, Research Triangle Park, Triangle Park, NC, United States
  38. 38. KU Leuven, Department of Public Health and Primary Care, Kortrijk, Belgium
  39. 39. Department of Biochemistry, University of Colorado, Boulder, CO, United States
  40. 40. Origent Data Sciences, Inc, Vienna, VA, United States
  41. 41. National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Huairou, China
  42. 42. Stanford University, Center for Biomedical Informatics Research, Stanford, CA, United States
  43. 43. Department of Statistics, Tel-Aviv University, Tel Aviv-Yafo, Israel
  44. 44. Helmholtz Zentrum Munchen, Institute of Health Economics and Health Care Management, Munich, Germany
  45. 45. Department of Bioinformatics, University of Bialystok, Ciolkowskiego, Bialystok, Poland
  46. 46. Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China
  47. 47. Microsoft Research New England, Cambridge, MA, United States
  48. 48. School of Computer, Wuhan University, Wuhan, China
  49. 49. Computational Centre, University of Bialystok, Ciolkowskiego, Bialystok, Poland
  50. 50. Children’s Mercy Hospital, Kansas City, MO, United States
  51. 51. LinkedIn, Sunnyville, CA, United States
  52. 52. Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego, Warsaw, Poland
  53. 53. Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  54. 54. Ceremade Universite Paris-Dauphine, Paris, France
  55. 55. Universite Paris-Est, Laboratoire d’Analyse et de Mathematiques Appliquees, Creteil, France
  56. 56. Stanford University, Department of Statistics, Stanford, CA, United States
  57. 57. Stanford University, Department of Electrical Engineering, Stanford, CA, United States
  58. 58. Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka
  59. 59. Department of Computer Science, University of California, Irvine, CA, United States
  60. 60. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, United States
  61. 61. Department of Computer Science, City University of Hong Kong, Hong Kong
  62. 62. Dept of Computer Science, Bren School of Information and Computer Sciences, University of California, Irvine, CA, United States
  63. 63. University of Pennsylvania, Philadelphia, PA, United States
  64. 64. University of Maryland, Baltimore, MD, United States
  65. 65. Centre for Computational System Biology, ISTBI, Fudan University, Shanghai, China

Source: Scientific Reports Published:2019


Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development. © The Author(s) 2019.
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