利用应用于临床非遗传或组织学数据的机器学习模型预测初级线粒体疾病的突变类型。

Q3 Medicine

Recenti progressi in medicina Pub Date : 2025-10-01 DOI:10.1701/4573.45801

Sara Mazzucato, Piervito Lopriore, Francesco Daddoveri, Costanza Lamperti, Valerio Carelli, Olimpia Musumeci, Serenella Servidei, Silvestro Micera, Michelangelo Mancuso, Andrea Bandini

{"title":"利用应用于临床非遗传或组织学数据的机器学习模型预测初级线粒体疾病的突变类型。","authors":"Sara Mazzucato, Piervito Lopriore, Francesco Daddoveri, Costanza Lamperti, Valerio Carelli, Olimpia Musumeci, Serenella Servidei, Silvestro Micera, Michelangelo Mancuso, Andrea Bandini","doi":"10.1701/4573.45801","DOIUrl":null,"url":null,"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.","PeriodicalId":20887,"journal":{"name":"Recenti progressi in medicina","volume":"116 10","pages":"613-614"},"PeriodicalIF":0.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predizione del tipo di mutazione nelle malattie mitocondriali primarie tramite modelli di machine learning applicati a dati clinici non genetici né istologici.\",\"authors\":\"Sara Mazzucato, Piervito Lopriore, Francesco Daddoveri, Costanza Lamperti, Valerio Carelli, Olimpia Musumeci, Serenella Servidei, Silvestro Micera, Michelangelo Mancuso, Andrea Bandini\",\"doi\":\"10.1701/4573.45801\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":20887,\"journal\":{\"name\":\"Recenti progressi in medicina\",\"volume\":\"116 10\",\"pages\":\"613-614\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Recenti progressi in medicina\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1701/4573.45801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recenti progressi in medicina","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1701/4573.45801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}

引用次数: 0

摘要

本研究表明，机器学习仅使用非遗传和非组织学临床数据就可以准确区分原发性线粒体疾病的线粒体和核DNA突变。虽然语言模型相比之下表现不佳，但它们显示出作为补充诊断工具的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Recenti progressi in medicina Medicine-Medicine (all)

CiteScore

0.90

自引率

0.00%

发文量

143

期刊介绍： Giunta ormai al sessantesimo anno, Recenti Progressi in Medicina continua a costituire un sicuro punto di riferimento ed uno strumento di lavoro fondamentale per l"ampliamento dell"orizzonte culturale del medico italiano. Recenti Progressi in Medicina è una rivista di medicina interna. Ciò significa il recupero di un"ottica globale e integrata, idonea ad evitare sia i particolarismi della informazione specialistica sia la frammentazione di quella generalista.