Format

Send to

Choose Destination
BMC Bioinformatics. 2015 Jul 23;16:226. doi: 10.1186/s12859-015-0643-8.

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

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. hao9@pitt.edu.
2
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. shv3@pitt.edu.
3
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA. shv3@pitt.edu.
4
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. xinghua@pitt.edu.
5
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.
6
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.
7
Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, USA. vanathi@pitt.edu.

Abstract

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.

PMID:
26202217
PMCID:
PMC4512094
DOI:
10.1186/s12859-015-0643-8
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center