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Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification

Pragya Verma1, Madhvi Shakya1

  • 1Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, 462003 India.

Cognitive Neurodynamics
|April 11, 2022
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Summary
This summary is machine-generated.

Machine learning enhances Major Depressive Disorder (MDD) classification by identifying 99 key genes. This approach reveals potential biomarkers and pathways, offering new avenues for diagnosing MDD and suicidal behavior.

Keywords:
ClassificationFeature selectionMachine learningRNA-Seq dataRandom forestk-nearest neighbor (KNN)

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Area of Science:

  • Neuroscience and Genetics
  • Computational Biology and Bioinformatics

Background:

  • Major Depressive Disorder (MDD) is a severe brain disorder linked to suicidal behavior, with current physical detection methods lacking precision.
  • Machine learning (ML) offers a promising approach to improve the classification and understanding of MDD by analyzing complex biological data.

Purpose of the Study:

  • To classify differentially expressed genes (DEGs) in Major Depressive Disorder (MDD) using RNA-seq data.
  • To train a machine learning model, specifically Random Forest (RF), to identify key genes and pathways associated with MDD and suicidal behavior.
  • To identify potential gene biomarkers for MDD.

Main Methods:

  • RNA-sequencing (RNA-seq) data from three groups: healthy controls (CON), Major Depressive Disorder with Suicide (MDD-S), and Major Depressive Disorder without Suicide (MDD).
  • Principal Component Analysis (PCA) to identify 99 key genes contributing to 47.1% of data variability.
  • Random Forest (RF) machine learning model for classification, with comparison to K-Nearest Neighbors (KNN). Gene annotation using DAVID and ClueGo.

Main Results:

  • The RF model achieved 61.11% accuracy on test data and 97.56% on training data, outperforming KNN.
  • Key pathways identified include glutamatergic synapse, GABA receptor activation, long-term synaptic depression, and morphine addiction.
  • Twelve genes were found to be dysregulated, with four (DLGAP1, GNG2, GRIA1, GRIA4) significantly involved in glutamatergic synapse, and others in GABA receptor activation, long-term synaptic depression, and morphine addiction.

Conclusions:

  • The study successfully identified 99 key genes and 12 potential gene biomarkers for MDD through machine learning analysis of RNA-seq data.
  • The identified genes and pathways provide novel insights into the biological mechanisms underlying MDD and suicidal behavior.
  • This ML-driven approach serves as a robust method for analyzing large datasets and advancing the diagnosis and understanding of MDD.