ArticleDisease markers2013
A semiautomated framework for integrating expert knowledge into disease marker identification.
Article in Disease markers, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures.Toxicology in vitro : an international journal published in association with BIBRA · 2020Article
- Comprehensive Proteomics Analysis of Stressed Human Islets Identifies GDF15 as a Target for Type 1 Diabetes Intervention.Cell metabolism · 2020Article
- Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks.Oncology letters · 2017Article
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14 authors.
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Abstract
backgroundThe 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.
objectiveTo 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.
methodsThe 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.
resultsThe 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.
conclusionsExpert 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.
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