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Related Concept Videos

Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
Human Genetics01:28

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Human Virome01:26

Human Virome

The human body harbors a vast and diverse viral community known as the human virome. The virome includes bacteriophages that infect bacteria, and eukaryotic viruses that infect human cells. Transient dietary and environmental viruses also contribute to this dynamic ecosystem. Estimates suggest the human body may contain on the order of 10¹³ viral particles, though abundance varies widely by body site and detection method.Comprehensive characterization of the virome has become possible only with...
Genetic Lingo01:11

Genetic Lingo

Overview
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jun 27, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Generative AI and Language Models in Human Genetics and Health: From Variant Interpretation to Clinical Decision

Yael Pinchevsky Itan1,2,3, Yuval Itan1,2,3,4

  • 1The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Genes
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (AI) is revolutionizing biological and medical research by generating novel data like protein sequences and clinical notes. However, these powerful AI tools also present risks, including biased outputs and potential inaccuracies.

Keywords:
clinical decision supportclinical genomicselectronic health recordsgenerative artificial intelligencegenomic language modelslarge language modelsprotein designretrieval-augmented generationsynthetic health datavariant interpretation

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Published on: August 15, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Medical Informatics

Background:

  • Generative artificial intelligence (AI) models are increasingly utilized in scientific research.
  • These models possess the capability to analyze complex patterns and generate novel data.

Purpose of the Study:

  • To explore the transformative impact of generative AI in biological and medical research.
  • To highlight the applications and potential risks associated with these advanced AI technologies.

Main Methods:

  • Application of language-like sequence models for DNA, RNA, and amino acid sequence analysis and generation.
  • Utilization of large language models (LLMs) trained on biomedical literature and electronic health records (EHRs).
  • Employing synthetic data generation techniques.

Main Results:

  • Generative AI enables prediction of genetic variant effects, design of new proteins, and exploration of molecular functions.
  • LLMs can summarize clinical findings, identify patterns, and support clinical decision-making.
  • Synthetic data generation aids in patient privacy protection and augmentation of disease datasets.

Conclusions:

  • Generative AI offers unprecedented capabilities for biological and medical research at scale.
  • Significant risks include the generation of inaccurate or biased results and underperformance in dynamic real-world conditions.
  • Careful consideration and mitigation strategies are essential to harness the benefits while managing the risks of generative AI.