We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
LGC Clinical Diagnostics

Download Mobile App




New AI Model Predicts Gene Variants’ Effects on Specific Diseases

By LabMedica International staff writers
Posted on 01 Apr 2025
Print article
Image: The AI model accurately identifies harmful genetic mutations for precise diagnoses and treatments (Photo courtesy of 123RF)
Image: The AI model accurately identifies harmful genetic mutations for precise diagnoses and treatments (Photo courtesy of 123RF)

In recent years, artificial intelligence (AI) has greatly enhanced our ability to identify a vast number of genetic variants in increasingly larger populations. However, up to half of these variants are classified as of uncertain significance, meaning their role in causing a disease, if any, remains unclear. Existing AI models are effective at distinguishing which gene variants are more likely to negatively impact protein structure or function, potentially leading to disease. However, these models lack the capacity to connect a specific genetic variant to a particular disease, limiting their usefulness in diagnosis and treatment. Now, researchers have developed a new AI model capable of accurately identifying harmful genetic mutations for more precise diagnoses and treatments.

The novel AI model, named DYNA, was developed by researchers at Cedars-Sinai (Los Angeles, CA, USA) and accurately differentiates between harmful and harmless gene variations, enhancing physicians' ability to diagnose diseases. This new tool has the potential to pave the way for more targeted and personalized medicine. In research published in the peer-reviewed journal Nature Machine Intelligence, the team demonstrated that DYNA outperforms existing AI models in predicting which DNA changes, commonly referred to as mutations, are linked to specific cardiovascular conditions and other diseases.

To create DYNA, the researchers employed a type of AI called a Siamese neural network to refine two existing AI models. These modified models were used to predict the likelihood that particular gene variants are associated with conditions such as cardiomyopathy (heart muscle enlargement, stiffening, or weakening) and arrhythmia (irregular heartbeat). The team then compared DYNA’s results to data from ClinVar, a reputable public database that collects reports of genetic variations linked to diseases. The comparison revealed that DYNA successfully matched the genetic variants with the corresponding diseases.

“For researchers, DYNA provides a flexible framework to study various genetic diseases,” said Jason Moore, PhD, a contributing author of the study and chair of the Department of Computational Biomedicine at Cedars-Sinai. “Future developments could include using DYNA to offer healthcare professionals advanced tools for tailoring diagnoses and treatments to each individual’s genetic profile.”

Gold Member
Chagas Disease Test
CHAGAS Cassette
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Biological Indicator Vials
BI-O.K.
New
Community-Acquired Pneumonia Test
RIDA UNITY CAP Bac

Print article

Channels

Clinical Chemistry

view channel
Image: The tiny clay-based materials can be customized for a range of medical applications (Photo courtesy of Angira Roy and Sam O’Keefe)

‘Brilliantly Luminous’ Nanoscale Chemical Tool to Improve Disease Detection

Thousands of commercially available glowing molecules known as fluorophores are commonly used in medical imaging, disease detection, biomarker tagging, and chemical analysis. They are also integral in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Microbiology

view channel
Image: The lab-in-tube assay could improve TB diagnoses in rural or resource-limited areas (Photo courtesy of Kenny Lass/Tulane University)

Handheld Device Delivers Low-Cost TB Results in Less Than One Hour

Tuberculosis (TB) remains the deadliest infectious disease globally, affecting an estimated 10 million people annually. In 2021, about 4.2 million TB cases went undiagnosed or unreported, mainly due to... Read more

Technology

view channel
Image: The HIV-1 self-testing chip will be capable of selectively detecting HIV in whole blood samples (Photo courtesy of Shutterstock)

Disposable Microchip Technology Could Selectively Detect HIV in Whole Blood Samples

As of the end of 2023, approximately 40 million people globally were living with HIV, and around 630,000 individuals died from AIDS-related illnesses that same year. Despite a substantial decline in deaths... Read more

Industry

view channel
Image: The collaboration aims to leverage Oxford Nanopore\'s sequencing platform and Cepheid\'s GeneXpert system to advance the field of sequencing for infectious diseases (Photo courtesy of Cepheid)

Cepheid and Oxford Nanopore Technologies Partner on Advancing Automated Sequencing-Based Solutions

Cepheid (Sunnyvale, CA, USA), a leading molecular diagnostics company, and Oxford Nanopore Technologies (Oxford, UK), the company behind a new generation of sequencing-based molecular analysis technologies,... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.