2012 TransductiveMultiLabelEnsembleC

• (Yu et al., 2012) ⇒ Guoxian Yu, Carlotta Domeniconi, Huzefa Rangwala, Guoji Zhang, and Zhiwen Yu. (2012). “Transductive Multi-label Ensemble Classification for Protein Function Prediction.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339700

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Transductive+Multi-label+Ensemble+Classification+for+Protein+Function+Prediction
• http://dl.acm.org/citation.cfm?id=2339530.2339700&preflayout=flat#citedby

Quotes

Author Keywords

• Biology and genetics; classifier design and evaluation; directed bi-relation graph; multi-label ensemble classifier; protein function prediction

Abstract

Advances in biotechnology have made available multitudes of heterogeneous proteomic and genomic data. Integrating these heterogeneous data sources, to automatically infer the function of proteins, is a fundamental challenge in computational biology. Several approaches represent each data source with a kernel (similarity) function. The resulting kernels are then integrated to determine a composite kernel, which is used for developing a function prediction model. Proteins are also found to have multiple roles and functions. As such, several approaches cast the protein function prediction problem within a multi-label learning framework. In our work we develop an approach that takes advantage of several unlabeled proteins, along with multiple data sources and multiple functions of proteins. We develop a graph-based transductive multi-label classifier(TMC) that is evaluated on a composite kernel, and also propose a method for data integration using the ensemble framework, called transductive multi-label ensemble classifier(TMEC). The TMEC approach trains a graph-based multi-label classifier for each individual kernel, and then combines the predictions of the individual models. Our contribution is the use of a bi-relational directed graph that captures relationships between pairs of proteins, between pairs of functions, and between proteins and functions. We evaluate the ability of TMC and TMEC to predict the functions of proteins by using two yeast datasets. We show that our approach performs better than recently proposed protein function prediction methods on composite and multiple kernels.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 TransductiveMultiLabelEnsembleC	Carlotta Domeniconi Guoxian Yu Huzefa Rangwala Guoji Zhang Zhiwen Yu			Transductive Multi-label Ensemble Classification for Protein Function Prediction				10.1145/2339530.2339700		2012