Molecular pathway identification using biological network-regularized logistic models

• Published in: BMC genomics Vol. 14; no. Suppl 8; p. S7
• Main Authors: Zhang, Wen, Wan, Ying-wooi, Allen, Genevera I, Pang, Kaifang, Anderson, Matthew L, Liu, Zhandong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.12.2013; Springer Nature B.V
• Subjects: Algorithms; Animal Genetics and Genomics; Biomarkers, Tumor - metabolism; Biomedical and Life Sciences; Breast cancer; Breast Neoplasms - metabolism; Computational Biology - methods; Computer Simulation; Female; Gene Regulatory Networks; Genomics; Humans; Life Sciences; Logistic Models; Microarrays; Microbial Genetics and Genomics; Models, Biological; Plant Genetics and Genomics; Proteomics; Reproducibility of Results; Studies; Breast Cancer Subtype; Triple Negative Breast Cancer; Normalize Read Count; Breast Cancer Specimen; Lasso
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-14-S8-S7

Background Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L 1 -norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature. Results We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease classification and pathway association problems. Simulation studies demonstrate that the performance of the proposed algorithm is superior to elastic net and lasso analyses. Utility of this algorithm is also validated by its ability to reliably differentiate breast cancer subtypes using a large breast cancer dataset recently generated by the Cancer Genome Atlas (TCGA) consortium. Many of the protein-protein interaction modules identified by our approach are further supported by evidence published in the literature. Source code of the proposed algorithm is freely available at http://www.github.com/zhandong/Logit-Lapnet . Conclusion Logistic regression with graph Laplacian regularization is an effective algorithm for identifying key pathways and modules associated with disease subtypes. With the rapid expansion of our knowledge of biological regulatory networks, this approach will become more accurate and increasingly useful for mining transcriptomic, epi-genomic, and other types of genome wide association studies.