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Enhancing selection of alcohol consumption-associated genes by random forest.
- Source :
- British Journal of Nutrition; 6/28/2024, Vol. 131 Issue 12, p2058-2067, 10p
- Publication Year :
- 2024
-
Abstract
- Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4 , IL4R , and SORT1 , and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts. [ABSTRACT FROM AUTHOR]
- Subjects :
- RANDOM forest algorithms
RISK assessment
CARDIOVASCULAR diseases
RECEIVER operating characteristic curves
DATA analysis
RESEARCH funding
HYPERTENSION
FISHER exact test
CARDIOVASCULAR diseases risk factors
DESCRIPTIVE statistics
GENE expression
GENE expression profiling
STATISTICS
TYPE 2 diabetes
ALCOHOL drinking
MACHINE learning
ONTOLOGIES (Information retrieval)
CONFIDENCE intervals
BIOMARKERS
REGRESSION analysis
OBESITY
PEOPLE with alcoholism
Subjects
Details
- Language :
- English
- ISSN :
- 00071145
- Volume :
- 131
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- British Journal of Nutrition
- Publication Type :
- Academic Journal
- Accession number :
- 178184895
- Full Text :
- https://doi.org/10.1017/S0007114524000795