A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification
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 »
- Authors:
-
- 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:
- 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.
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
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.
@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}
}
https://doi.org/10.1155/2013/613529
Web of Science