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Open Access Research

Protein network-based Lasso regression model for the construction of disease-miRNA functional interactions

Ala Qabaja1, Mohammed Alshalalfa12*, Tarek A Bismar3 and Reda Alhajj1

Author Affiliations

1 Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

2 Biotechnology Research Center, Palestine Polytechnic University, Hebron, Palestine

3 Departments of Pathology, Oncology and Molecular Biology and Biochemistry, Faculty of Medicine, University of Calgary, Alberta, Canada

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EURASIP Journal on Bioinformatics and Systems Biology 2013, 2013:3  doi:10.1186/1687-4153-2013-3

Published: 22 January 2013

Abstract

Background

There is a growing body of evidence associating microRNAs (miRNAs) with human diseases. MiRNAs are new key players in the disease paradigm demonstrating roles in several human diseases. The functional association between miRNAs and diseases remains largely unclear and far from complete. With the advent of high-throughput functional genomics techniques that infer genes and biological pathways dysregulted in diseases, it is now possible to infer functional association between diseases and biological molecules by integrating disparate biological information.

Results

Here, we first used Lasso regression model to identify miRNAs associated with disease signature as a proof of concept. Then we proposed an integrated approach that uses disease-gene associations from microarray experiments and text mining, and miRNA-gene association from computational predictions and protein networks to build functional associations network between miRNAs and diseases. The findings of the proposed model were validated against gold standard datasets using ROC analysis and results were promising (AUC=0.81). Our protein network-based approach discovered 19 new functional associations between prostate cancer and miRNAs. The new 19 associations were validated using miRNA expression data and clinical profiles and showed to act as diagnostic and prognostic prostate biomarkers. The proposed integrated approach allowed us to reconstruct functional associations between miRNAs and human diseases and uncovered functional roles of newly discovered miRNAs.

Conclusions

Lasso regression was used to find associations between diseases and miRNAs using their gene signature. Defining miRNA gene signature by integrating the downstream effect of miRNAs demonstrated better performance than the miRNA signature alone. Integrating biological networks and multiple data to define miRNA and disease gene signature demonstrated high performance to uncover new functional associations between miRNAs and diseases.

Keywords:
miRNA; Protein interactions; Systems biology; Disease; Regression modeling