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Related Concept Videos

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Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Gene2vec: distributed representation of genes based on co-expression.

Jingcheng Du1, Peilin Jia1, Yulin Dai1

  • 1The University of Texas School of Biomedical Informatics, Houston, TX, 77030, USA.

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|February 5, 2019
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We developed a machine learning approach to create distributed gene representations from gene co-expression data. These gene embeddings capture functional relationships and improve gene-gene interaction prediction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Current gene function descriptions are categorical and manually curated.
  • This study explores gene embedding, a distributed representation of genes, inspired by word embedding techniques.

Purpose of the Study:

  • To develop a data-driven method for generating distributed gene representations.
  • To capture functional relatedness and improve gene-gene interaction prediction.

Main Methods:

  • Trained 200-dimension vector representations for all human genes.
  • Utilized gene co-expression patterns from 984 Gene Expression Omnibus (GEO) datasets.
  • Employed t-distributed Stochastic Neighbor Embedding (t-SNE) for visualization.

Main Results:

  • Gene vectors captured functional relatedness, with intra-pathway gene similarity 1.52X greater than random genes.
  • A gene co-expression map revealed concentrations of tissue-specific genes.
  • Demonstrated utility in predicting gene-gene interactions using embedded gene vectors.

Conclusions:

  • Proposed a machine learning method for distributed gene representation using transcriptome-wide co-expression.
  • Showcased the utility of gene embeddings in predicting gene-gene interactions.
  • Highlighted the potential of distributed gene representations for broader bioinformatics applications.