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      Convergence of a generalized belief propagation algorithm for biological networks

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      https://www.riss.kr/link?id=A108140808

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      다국어 초록 (Multilingual Abstract)

      A factor graph and belief propagation can be used for finding stochastic values of link weights in biological networks. However it is not easy to follow the process of use and so we presented the process with a toy network of three nodes in our prior...

      A factor graph and belief propagation can be used for finding stochastic values of link weights in biological networks. However it is not easy to follow the process of use and so we presented the process with a toy network of three nodes in our prior work. We extend this work more generally and present numerical example for a network of 100 nodes.

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      참고문헌 (Reference)

      1 J. D. J. Hand, "Understanding biological functions through molecular networks" 18 : 224-237, 2008

      2 J. S. Yedidia, "Understanding belief propagation and its generalizations" 8 : 236-239, 2003

      3 H. A. Loeliger, "The factor graph approach to model-based signal processing" 95 : 1295-1322, 2007

      4 A. Braustein, "Survey propagation : An algorithm for satisfiablility" 27 : 201-226, 2005

      5 J. Pearl, "Probabilistic Reasoning in Intelligent systems: Networks of Plausible Inference" Morgan Kaufmann 1988

      6 E. J. Molinelli, "Perturbation biology: inferring signaling networks in cellular systems" 9 : 2013

      7 A. Korkut, "Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells" 4 : 2015

      8 C. M. Bishop, "Pattern recognition and machine learning" springer 2006

      9 Q. Su, "On Convergence Conditions of Gaussian Belief Propagation" 63 : 1144-1155, 2015

      10 D. R. Hunter, "Notes for a Graduate-Level Course in Asymptotics for Statics" Penn State University 2014

      1 J. D. J. Hand, "Understanding biological functions through molecular networks" 18 : 224-237, 2008

      2 J. S. Yedidia, "Understanding belief propagation and its generalizations" 8 : 236-239, 2003

      3 H. A. Loeliger, "The factor graph approach to model-based signal processing" 95 : 1295-1322, 2007

      4 A. Braustein, "Survey propagation : An algorithm for satisfiablility" 27 : 201-226, 2005

      5 J. Pearl, "Probabilistic Reasoning in Intelligent systems: Networks of Plausible Inference" Morgan Kaufmann 1988

      6 E. J. Molinelli, "Perturbation biology: inferring signaling networks in cellular systems" 9 : 2013

      7 A. Korkut, "Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells" 4 : 2015

      8 C. M. Bishop, "Pattern recognition and machine learning" springer 2006

      9 Q. Su, "On Convergence Conditions of Gaussian Belief Propagation" 63 : 1144-1155, 2015

      10 D. R. Hunter, "Notes for a Graduate-Level Course in Asymptotics for Statics" Penn State University 2014

      11 K. P. Murphy, "Machine learning: a probabilistic perspective" MIT press 2012

      12 A. Braunstein, "Inference algorithm for gene networks: a statical mechanics analysis" 2008 : 2008

      13 F. R. Kschischang, "Factor graphs and the sum-product algorithm" 47 : 498-519, 2001

      14 S. -M. Choo, "Convergence of a Belief Propagation Algorithm for Biological Network" 2019 : 2019

      15 W. Materi, "Computational systems biology in cancer : modeling methods and applications" 1 : 91-110, 2007

      16 H. F. Fumia, "Boolean network model for cancer pathways : predicting carcinogenesis and targeted therapy outcomes" 8 : e69008-, 2013

      17 M. A. Khamsi, "An introduction to metric spaces and fixed point theory" J. Wiley and Sons 2011

      18 H. A. Loeliger, "An introduction to factor graphs" 21 : 28-41, 2004

      19 T. Heskes, "Advances in neural information processing systems" 359-366, 2003

      20 R. Barbuti, "A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models" 2 : 1-20, 2020

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
      2020-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2019-11-08 학회명변경 영문명 : The Korean Society For Computational & Applied Mathematics And Korean Sigcam -> Korean Society for Computational and Applied Mathematics KCI등재
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-02-18 학술지명변경 한글명 : Journal of Applied Mathematics and Infomatics(Former: Korean J. of Comput. and Appl. Math.) -> Journal of Applied Mathematics and Informatics
      외국어명 : Journal of Applied Mathematics and Infomatics(Former: Korean J. of Comput. and Appl. Math.) -> Journal of Applied Mathematics and Informatics
      KCI등재
      2008-02-15 학술지명변경 한글명 : Journal of Applied Mathematics and Computing(Former: Korean J. of Comput. and Appl. Math.) -> Journal of Applied Mathematics and Infomatics(Former: Korean J. of Comput. and Appl. Math.)
      외국어명 : Journal of Applied Mathematics and Computing(Former: Korean J. of Comput. and Appl. Math.) -> Journal of Applied Mathematics and Infomatics(Former: Korean J. of Comput. and Appl. Math.)
      KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2001-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1998-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.16 0.16 0.13
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.1 0.07 0.312 0.02
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