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What is Gene Expression?

What is Gene Expression?

Overview
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.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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General Transcription Factors

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

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Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based

Ting-He Zhang¹, Md Musaddaqul Hasib¹, Yu-Chiao Chiu^2,3

¹Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA.

|October 14, 2022

Summary

We developed a new interpretable deep learning model, Transformer for Gene Expression Modeling (T-GEM), for analyzing gene expression data in precision oncology. T-GEM effectively predicts cancer phenotypes and reveals gene interactions, advancing transcriptomics research.

Keywords:

Transformer cancer type prediction immune cell type prediction interpretable deep learning phenotypes prediction

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Deep learning (DL) models, often inspired by computer vision, face challenges with the unique characteristics of gene expression data in precision oncology.
Existing DL models may lack interpretability, hindering their application in transcriptomics studies for phenotype prediction.

Purpose of the Study:

To propose a novel interpretable deep learning architecture, Transformer for Gene Expression Modeling (T-GEM), specifically designed for gene expression data.
To demonstrate T-GEM's capability in modeling gene-gene interactions and predicting cancer-related phenotypes.

Main Methods:

Developed the T-GEM architecture, a Transformer-based model tailored for transcriptomics.
Applied T-GEM to gene expression data for cancer type prediction and immune cell type classification.
Analyzed T-GEM's attention mechanisms to understand its learning process and identify phenotype-related genes.
Devised a method to extract gene regulatory networks learned by T-GEM using self-attention weights.

Main Results:

T-GEM effectively models gene-gene interactions and predicts cancer-related phenotypes, including cancer type and immune cell types.
Analysis revealed that T-GEM's attention shifts from broad gene interactions in early layers to focused, phenotype-specific genes in higher layers.
The model's self-attention mechanism captures biologically relevant functions associated with predicted phenotypes.
Extracted regulatory networks highlighted potential marker genes crucial for the predicted phenotypes.

Conclusions:

T-GEM offers an interpretable deep learning approach for gene expression data analysis in precision oncology.
The model's ability to predict phenotypes and elucidate gene regulatory networks advances the application of AI in genomics.
T-GEM's interpretability facilitates the discovery of novel biomarkers and biological insights from transcriptomic data.