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Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data

Ogoe, HA and Visweswaran, S and Lu, X and Gopalakrishnan, V (2015) Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data. BMC Bioinformatics, 16 (1).

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Background: Most 'transcriptomic' data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, 'transcriptomic' data may help build better classification models. However, most proposed methods for integrative analysis of 'transcriptomic' data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that "an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources". Results: To evaluate the feasibility of the TRL-FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and other traditional methods, using 21 microarray datasets generated from three studies on brain cancer, prostate cancer, and lung disease, respectively. The results show that TRL-FM statistically significantly outperforms TRL as well as traditional models based on single source data. In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging. Conclusions: The capability of utilizing transferred abstract knowledge derived from source data using feature mapping enables the TRL-FM framework to mimic the human process of learning and adaptation when performing related tasks. The novel TRL-FM methodology for integrative modeling for multiple 'transcriptomic' datasets is able to intelligently incorporate domain knowledge that traditional methods might disregard, to boost predictive power and generalization performance. In this study, TRL-FM's abstraction of knowledge is achieved in the form of functional modules, but the overall framework is generalizable in that different approaches of acquiring abstract knowledge can be integrated into this framework.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Ogoe, HAhao9@pitt.eduHAO9
Visweswaran, Sshv3@pitt.eduSHV3
Lu, Xxinghua@pitt.eduXINGHUA
Gopalakrishnan, Vvanathi@pitt.eduVANATHI
Date: 23 July 2015
Date Type: Publication
Journal or Publication Title: BMC Bioinformatics
Volume: 16
Number: 1
DOI or Unique Handle: 10.1186/s12859-015-0643-8
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Medicine > Biomedical Informatics
School of Medicine > Computational and Systems Biology
Refereed: Yes
Date Deposited: 17 Aug 2016 13:44
Last Modified: 30 Mar 2021 13:56


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