A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

doi:10.1155/2013/613529

Title: A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

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Abstract

Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from largemore »« less

Authors:

Wang, Jing ^[1]; Webb-Robertson, Bobbie-Jo M. ^[1]; Matzke, Melissa M. ^[1]; Varnum, Susan M. ^[2]; Brown, Joseph N. ^[2]; Riensche, Roderick M. ^[3]; Adkins, Joshua N. ^[2]; Jacobs, Jon M. ^[2]; Hoidal, John R. ^[4]; Scholand, Mary Beth ^[4]; Pounds, Joel G. ^[2]; Blackburn, Michael R. ^[5]; Rodland, Karin D. ^[2]; McDermott, Jason E. ^[1]

Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Knowledge Discovery and Informatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
Department of Biochemistry and Molecular Biology, University of Texas Medical School, Houston, TX 77030, USA

Publication Date:: Tue Jan 01 00:00:00 EST 2013

Sponsoring Org.:: USDOE

OSTI Identifier:: 1198323

Grant/Contract Number:: AC05-76RL01830

Resource Type:: Published Article

Journal Name:: Disease Markers

Additional Journal Information:: Journal Name: Disease Markers Journal Volume: 35; Journal ID: ISSN 0278-0240

Publisher:: Hindawi Publishing Corporation

Country of Publication:: Country unknown/Code not available

Language:: English

Citation Formats


                    Wang, Jing, Webb-Robertson, Bobbie-Jo M., Matzke, Melissa M., Varnum, Susan M., Brown, Joseph N., Riensche, Roderick M., Adkins, Joshua N., Jacobs, Jon M., Hoidal, John R., Scholand, Mary Beth, Pounds, Joel G., Blackburn, Michael R., Rodland, Karin D., and McDermott, Jason E. A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification.  Country unknown/Code not available: N. p., 2013. 
Web.  doi:10.1155/2013/613529.

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                    Wang, Jing, Webb-Robertson, Bobbie-Jo M., Matzke, Melissa M., Varnum, Susan M., Brown, Joseph N., Riensche, Roderick M., Adkins, Joshua N., Jacobs, Jon M., Hoidal, John R., Scholand, Mary Beth, Pounds, Joel G., Blackburn, Michael R., Rodland, Karin D., & McDermott, Jason E. A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification.  Country unknown/Code not available.  https://doi.org/10.1155/2013/613529

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                    Wang, Jing, Webb-Robertson, Bobbie-Jo M., Matzke, Melissa M., Varnum, Susan M., Brown, Joseph N., Riensche, Roderick M., Adkins, Joshua N., Jacobs, Jon M., Hoidal, John R., Scholand, Mary Beth, Pounds, Joel G., Blackburn, Michael R., Rodland, Karin D., and McDermott, Jason E. Tue .  
"A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification".  Country unknown/Code not available.  https://doi.org/10.1155/2013/613529.

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@article{osti_1198323,

  title        = {A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification},

  author       = {Wang, Jing and Webb-Robertson, Bobbie-Jo M. and Matzke, Melissa M. and Varnum, Susan M. and Brown, Joseph N. and Riensche, Roderick M. and Adkins, Joshua N. and Jacobs, Jon M. and Hoidal, John R. and Scholand, Mary Beth and Pounds, Joel G. and Blackburn, Michael R. and Rodland, Karin D. and McDermott, Jason E.},

  abstractNote = {Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.},

  doi          = {10.1155/2013/613529},

  journal      = {Disease Markers},

  number       = ,

  volume       = 35,

  place        = {Country unknown/Code not available},

  year         = {Tue Jan 01 00:00:00 EST 2013},

  month        = {Tue Jan 01 00:00:00 EST 2013}

}

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