A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification
Joint Authors
Mcdermott, Jason E.
Wang, Jing
Webb-Robertson, Bobbie-Jo M.
Matzke, Melissa M.
Varnum, Susan M.
Brown, Joseph N.
Riensche, Roderick M.
Jacobs, Jon M.
Hoidal, John R.
Scholand, Mary Beth
Pounds, Joel G.
Blackburn, Michael R.
Rodland, Karin D.
Adkins, Joshua N.
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-10
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Jing& Webb-Robertson, Bobbie-Jo M.& Matzke, Melissa M.& Varnum, Susan M.& Brown, Joseph N.& Riensche, Roderick M.…[et al.]. 2013. A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification. Disease Markers،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1031212
Modern Language Association (MLA)
Wang, Jing…[et al.]. A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification. Disease Markers No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-1031212
American Medical Association (AMA)
Wang, Jing& Webb-Robertson, Bobbie-Jo M.& Matzke, Melissa M.& Varnum, Susan M.& Brown, Joseph N.& Riensche, Roderick M.…[et al.]. A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification. Disease Markers. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1031212
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031212