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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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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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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Related Experiment Video

Updated: Jan 29, 2026

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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AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.

Ema Andreea Pălăștea1,2, Irina-Mihaela Matache2, Eugen Radu2,3

  • 1Genomics Research and Development Institute, Bucharest 030167, Romania.

International Journal of Molecular Sciences
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Omics research, powered by artificial intelligence and quantum computing, is advancing personalized medicine by improving the analysis of complex biological data for more accurate diagnoses and treatments.

Keywords:
AI-based predictiondeep learninggene expressionmachine learningquantum computingsingle-cell omicsspatial transcriptomics

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Precision Medicine

Background:

  • Omics research generates vast amounts of complex biological data.
  • Existing analytical methods struggle with the scale and complexity of multi-omics datasets.
  • Personalized medicine requires advanced tools for interpreting this data.

Purpose of the Study:

  • To explore how advanced computational tools, including AI and quantum algorithms, can enhance multi-omics data analysis.
  • To improve the prediction of gene expression profiles for better diagnostic and therapeutic strategies.
  • To facilitate the integration and interrogation of large-scale omics data for clinical decision-making.

Main Methods:

  • Application of advanced machine learning models, particularly deep learning (DL) networks, for omics analysis.
  • Exploration of quantum computing and quantum machine learning for predictive modeling of complex gene interactions.
  • Development of computational frameworks to handle large, high-dimensional biological datasets.

Main Results:

  • AI and quantum algorithms show potential in enhancing multi-omics analyses for efficiency and reliability.
  • Deep learning networks excel at recognizing patterns and modeling non-linear relationships in gene expression.
  • These computational advancements reduce error rates and improve the interpretation of large omics datasets.

Conclusions:

  • The integration of high-resolution omics data with advanced computational tools marks a significant shift in clinical decision-making.
  • Innovations in AI and quantum computing are crucial for realizing the full potential of personalized medicine.
  • Faster, more precise analytical workflows enable data-driven approaches to diagnosis, prevention, and treatment.