Cited by 6Treatment of Major DepressionGene expression and cancer classificationGenetic Associations and Epidemiology

Developing a Genetic Biomarker-Based Diagnostic Model for Major Depressive Disorder Using Random Forests and Artificial Neural Networks

Xie, Zhongwen, Gu, Wei, Ming, Tinghong·2023·Combinatorial Chemistry & High Throughput Screening

Abstract

BACKGROUND: The clinical diagnosis of major depressive disorder (MDD) mainly relies on subjective assessment of depression-like behaviors and clinical examination. In the present study, we aimed to develop a novel diagnostic model for specially predicting MDD. METHODS: The human brain GSE102556 DataSet and the blood GSE98793 and GSE76826 Data Sets were downloaded from the Gene Expression Omnibus (GEO) database. We used a novel algorithm, random forest (RF) plus artificial neural network (ANN), to examine gene biomarkers and establish a diagnostic model of MDD. RESULTS: Through the "limma" package in the R language, 2653 differentially expressed genes (DEGs) were identified in the GSE102556 DataSet, and 1786 DEGs were identified in the GSE98793 DataSet, and a total of 100 shared DEGs. We applied GSE98793 TrainData 1 to an RF algorithm and thereby successfully selected 28 genes as biomarkers. Furthermore, 28 biomarkers were verified by GSE98793 TestData 1, and the performance of these biomarkers was found to be perfect. In addition, we further used an ANN algorithm to optimize the weight of each gene and employed GSE98793 TrainData 2 to build an ANN model through the neural net package by R language. Based on this algorithm, GSE98793 TestData 2 and independent blood GSE76826 were verified to correlate with MDD, with AUCs of 0.903 and 0.917, respectively. CONCLUSION: To the best of our knowledge, this is the first time that the classifier constructed via DEG biomarkers has been used as an endophenotype for MDD clinical diagnosis. Our results may provide a new entry point for the diagnosis, treatment, outcome prediction, prognosis and recurrence of MDD.

Integrity signals

Registry

Crossref · 10.2174/1386207325666220404123433

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Xie, Z., Gu, W., & Ming, T.
Affiliations
  • Anhui Agricultural University (CN)
  • Ningbo University (CN)
Corresponding author
Xie, Zhongwen
Venue
Combinatorial Chemistry & High Throughput Screening
Published
2023-02
Volume
26
Issue
2
Pages
424-435
ISSN
1386-2073 (ISSN-L)
OpenAlex
W4225900886
Cited by
6
References
51
Citations by year
2025: 2 · 2024: 2 · 2023: 1 · 2022: 1
FWCI
0.90
Type
journal article
Language
en
Open access
closed
Locations
  • Combinatorial Chemistry & High Throughput Screening (publishedVersion)
  • PubMed (publishedVersion)
Funders
University Synergy Innovation Program of Anhui Province (GXXT-2019-49)
Topics
  • Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
major depressive disorder, genetic biomarkers, diagnostic model, differentially expressed genes, blood gene expression, brain gene expression, random forest, artificial neural networks, gene selection, endophenotypes, depression diagnosis
MeSH terms
Random Forest, Major Depressive Disorder, Genetic Markers, Humans, Neural Networks, Computer, Gene Expression Profiling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review