Publication

Predizione del tipo di mutazione nelle malattie mitocondriali primarie tramite modelli di machine learning applicati a dati clinici non genetici né istologici

2025

Abstract

This study shows that machine learning can accurately distinguish between mitochondrial and nuclear DNA mutations in primary mitochondrial diseases using only non-genetic and non-histological clinical data. While language models underperform in comparison, they show potential as complementary diagnostic tools.

Official source

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Predizione del tipo di mutazione nelle malattie mitocondriali primarie tramite modelli di machine learning applicati a dati clinici non genetici né istologici

Chat with Graph Search

ID #894 Landscape of cis-regulatory variation in pediatric-type diffuse high-grade glioma

Genome-wide association study of untargeted plasma metabolomic profiles identifies host genetic regulation in people with HIV

Computational Inference of Metabolic Programs: A Case Study Analyzing the Effect of BRCA1 Loss