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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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

Tianci Song^1,2, Yan Wang², Wei Du^1,2

¹* 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.

Journal of Bioinformatics and Computational Biology

|December 1, 2016

Summary

Predicting breast cancer grade using multiple data types improves early detection and treatment guidance. Our novel multiple kernel learning (MKL) approach integrates heterogeneous omics data and biological pathways for accurate classification.

Keywords:

Multiple kernel learning (MKL)biological interpretation breast cancer grade feature selection omics data integration

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Breast cancer histologic grade is crucial for treatment planning and prognosis.
Accurate grade prediction can enhance early detection and guide treatment strategies.
Integrating diverse data types offers deeper biological insights beyond single-modality approaches.

Purpose of the Study:

To develop a breast cancer grading predictor by integrating heterogeneous data.
To leverage multiple kernel learning (MKL) for improved classification accuracy.
To incorporate biological pathway information for enhanced interpretability.

Main Methods:

Utilized multiple kernel learning (MKL), a sophisticated supervised learning method.
Fused heterogeneous data types, including omics data, for breast cancer histopathology classification.
Modified the MKL model to include biological pathway information for pathway significance evaluation.

Main Results:

The proposed MKL-based method achieved superior performance compared to existing state-of-the-art methods.
The model successfully integrated omics data and pathway information for accurate breast cancer grading.
Demonstrated significant biological interpretation in explaining differences between breast cancer grades.

Conclusions:

The novel MKL approach effectively integrates heterogeneous omics data and pathway information for breast cancer grading.
This method provides a valuable tool for bridging omics data and clinical phenotypes.
Offers an auxiliary method for cancer mechanism research by integrating omics data.