Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.

Runyu Jing,Yanjie Wei,Li He,Yu Liang,Yi Ran,Shengzhong Feng

doi:10.1155/2018/8124950

Abstract

In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.

Highlights

With the development of genetic sequencing technology, genetic information could be recorded as gene expression data
The prediction performance was ascertained in two ways: by the modeling method class defined by WEKA and by the different subdatasets generated by the different variable selection methods
In the KIRCmRNA group, the distributions were similar in the two Breast invasive carcinoma (BRCA) groups but the lazy class performed in a similar manner in the meta and trees classes

Summary

Introduction

With the development of genetic sequencing technology, genetic information could be recorded as gene expression data. Data mining, such as using machine learning methods, is commonly used to reveal latent correlations between diseases and gene expression. The microarray quality control (MAQC) project thoroughly investigated the performance of models for the prediction of clinical outcomes of breast cancer, multiple myeloma, and neuroblastoma and were common practices for microarray-based model construction and validation [16]. To the best of our knowledge, no studies have examined multiple algorithms with two different kinds of expression data and their ensemble performance with a limited number of samples, which might be crucial when using them in practical. The number of available samples is restricted due to the International Journal of Genomics

Objectives

Methods

Results

Discussion

Conclusion