Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure

  • Authors:

  • Darrin P. Lewis;Tony Jebara;William Stafford Noble

  • Affiliations:

  • Department of Computer Science, Columbia University New York, NY, 10027;Department of Computer Science, Columbia University New York, NY, 10027;Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington Seattle, WA, 98195, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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Abstract

Motivation: Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Furthermore, several recently described extensions allow the SVM to assign relative weights to various datasets, depending upon their utilities in performing a given classification task. Results: In this work, we empirically investigate the performance of the SVM on the task of inferring gene functional annotations from a combination of protein sequence and structure data. Our results suggest that the SVM is quite robust to noise in the input datasets. Consequently, in the presence of only two types of data, an SVM trained from an unweighted combination of datasets performs as well or better than a more sophisticated algorithm that assigns weights to individual data types. Indeed, for this simple case, we can demonstrate empirically that no solution is significantly better than the naive, unweighted average of the two datasets. On the other hand, when multiple noisy datasets are included in the experiment, then the naive approach fares worse than the weighted approach. Our results suggest that for many applications, a naive unweighted sum of kernels may be sufficient. Availability: http://noble.gs.washington.edu/proj/seqstruct Contact: noble@gs.washington.edu Supplementary information: Supplementary Data are available at Bioinformatics online.