Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

Abstract

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P < 0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis

“…In this study, we set the random seed at 234 with the set.seed function to separate the training and testing sets, and the microarrays were randomly divided into the training and testing sets in a ratio of 8:2. In fact, we found that the segmentation of the training and testing data has different division standards in the different articles, such as 90:10 [ 48 ], 85:15 [ 49 ], 80:20, 70:30 [ 50 ], 60:40 [ 51 ], and so on. We choose 8:2 for three reasons: first, it is supported by the reported literature; second, considering that the number of samples is not large enough, we wanted to make the training data for developing the model as large as possible; third, the ratio of the number of DILI samples and control samples is about 80:20.…”

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

“…Many researchers (16)(17)(18) only focus on single or two MLs which might ignore their potential shortcomings. In our previous research (19), five MLs show different weights even with the same genes. So just intersecting the top N genes may unconsciously delete some dominant genes (20)(21)(22)(23).…”

“…So just intersecting the top N genes may unconsciously delete some dominant genes (20)(21)(22)(23). And ignoring the weights of genes may result in an imbalance of filtration (19,24).…”

Integration of Machine Learning to Identify Diagnostic Genes in Leukocytes for Acute Myocardial Infarction Patients

“…For the same genes, limma (version 3.54.0) was employed to identify the DEGs with the average gene expression. According to the Benjamini and Hochberg method, two thresholds were established: an absolute value of fold change (|logFC|) >0.7 (previous studies were 0.5 [29]-1 [23]) and a false discovery rate [30] <0.05.…”