Evolutionary Optimization of Kernel Weights Improves Protein Complex Comembership Prediction

In recent years, more and more high-throughput data sources useful for protein complex prediction have become available (e.g., gene sequence, mRNA expression, and interactions). The integration of these different data sources can be challenging. Recently, it has been recognized that kernel-based cla...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 6; no. 3; pp. 427 - 437
Main Authors: Hulsman, M., Reinders, M.J.T., de Ridder, D.
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial Intelligence
Bioinformatics
biology and genetics
Biology computing
Classifier design and evaluation
Evolution, Molecular
Evolutionary computation
evolutionary computing and genetic algorithms
Kernel
Large-scale systems
Linear Models
Models, Genetic
Multiprotein Complexes - chemistry
Nonlinear Dynamics
Optimization methods
Protein engineering
Reproducibility of Results
ROC Curve
Sequences
Studies
Support vector machine classification
Support vector machines
ISSN:1545-5963, 1557-9964, 1557-9964
Online Access:Get full text
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Summary:In recent years, more and more high-throughput data sources useful for protein complex prediction have become available (e.g., gene sequence, mRNA expression, and interactions). The integration of these different data sources can be challenging. Recently, it has been recognized that kernel-based classifiers are well suited for this task. However, the different kernels (data sources) are often combined using equal weights. Although several methods have been developed to optimize kernel weights, no large-scale example of an improvement in classifier performance has been shown yet. In this work, we employ an evolutionary algorithm to determine weights for a larger set of kernels by optimizing a criterion based on the area under the ROC curve. We show that setting the right kernel weights can indeed improve performance. We compare this to the existing kernel weight optimization methods (i.e., (regularized) optimization of the SVM criterion or aligning the kernel with an ideal kernel) and find that these do not result in a significant performance improvement and can even cause a decrease in performance. Results also show that an expert approach of assigning high weights to features with high individual performance is not necessarily the best strategy.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2008.137