Grouped Gene Selection of Cancer via Adaptive Sparse Group Lasso Based on Conditional Mutual Information

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 15; no. 6; pp. 2028 - 2038
• Main Authors: Li, Juntao, Dong, Wenpeng, Meng, Deyuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological information theory; Cancer; Cancer classification; Classification; conditional mutual information; DNA microarrays; Entropy; Gene expression; Genes; Genetics; group lasso; grouped gene selection; Mutual information; weighted gene co-expression network
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2017.2761871

This paper deals with the problems of cancer classification and grouped gene selection. The weighted gene co-expression network on cancer microarray data is employed to identify modules corresponding to biological pathways, based on which a strategy of dividing genes into groups is presented. Using the conditional mutual information within each divided group, an integrated criterion is proposed and the data-driven weights are constructed. They are shown with the ability to evaluate both the individual gene significance and the influence to improve correlation of all the other pairwise genes in each group. Furthermore, an adaptive sparse group lasso is proposed, by which an improved blockwise descent algorithm is developed. The results on four cancer data sets demonstrate that the proposed adaptive sparse group lasso can effectively perform classification and grouped gene selection.