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The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning

College of Computer Science and Technology, Key Laboratory of Symbolic, Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China

Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

E-mail Address: [email protected]

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Yan Wang

Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

E-mail Address: [email protected]

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Wei Du

Corresponding author.

College of Computer Science and Technology, Key Laboratory of Symbolic, Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China

Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

E-mail Address: [email protected]

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Sha Cao

Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

E-mail Address: [email protected]

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Yuan Tian

College of Computer Science and Technology, Key Laboratory of Symbolic, Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China

E-mail Address: [email protected]

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and

Yanchun Liang

Corresponding author.

College of Computer Science and Technology, Key Laboratory of Symbolic, Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China

Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, P. R. China

E-mail Address: [email protected]

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https://doi.org/10.1142/S0219720016500372Cited by:4

Abstract

Breast cancer histologic grade represents the morphological assessment of the tumor’s malignancy and aggressiveness, which is vital in clinically planning treatment and estimating prognosis for patients. Therefore, the prediction of breast cancer grade can markedly elevate the detection of early breast cancer and efficiently guide its treatment. With the advent of high-throughput profiling technology, a large number of data of different types are rapidly generated, and each data provides its unique biological insight. Although many researches focused on cancer grade prediction, hardly most of them attempted to integrate multiple data types, by which we cannot only improve and boost results obtained from learning method, but also have a good understanding or explanation of biological issues. In this paper, we take advantage of a sophisticated supervised learning method called multiple kernel learning (MKL) to design a breast cancer grading predictor fusing heterogeneous data for classification of breast cancer histopathology. Furthermore, we modify our model by involving biological pathway information. The new model can evaluate the significance of various pathways in which differential expression genes fall between different breast cancer grades. The merits of the novel model are lucubration in bridging between omics data and various phenotypes of breast cancer grades, and providing an auxiliary method integrating omics data of cancer mechanism research. In experiments, the proposed method outperforms other state-of-the-art methods and has abundant biological interpretation in explaining differences between breast cancer grades.

Keywords:

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Published: 30 November 2016

Vol. 15, No. 01

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History

Received 29 April 2016

Revised 8 September 2016

Accepted 13 October 2016

Keywords

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The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning

Abstract

References

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