Predicting Protein Function Using Multiple Kernels

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 12; no. 1; pp. 219 - 233
• Main Authors: Guoxian Yu, Rangwala, Huzefa, Domeniconi, Carlotta, Guoji Zhang, Zili Zhang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Bioinformatics; Computational biology; Humans; Kernel; Methods; Mice; Noise measurement; Protein Interaction Maps; Proteins; Proteins - chemistry; Proteins - classification; Proteomics - methods; Sequence Analysis, Protein; Yeasts - genetics; Protein function prediction; multiple kernels; multi-label learning
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2014.2351821

High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.