title: The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning.
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.
authors.affiliation: * 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.
authors.forename: Tianci
authors.initials: T
authors.lastname: Song
authors.affiliation: † Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: * 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.
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.affiliation: † Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.
authors.forename: Sha
authors.initials: S
authors.lastname: Cao
authors.affiliation: * 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.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Tian
authors.affiliation: * 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.
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology*; D019175:DNA Methylation; D016208:Databases, Factual; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D060787:Neoplasm Grading / Q000379:methods*; D015398:Signal Transduction; D060388:Support Vector Machine
keywords: Multiple kernel learning (MKL); biological interpretation; breast cancer grade; feature selection; omics data integration