Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Osr1-expressing mesoderm contributes to lymphatic vessel assembly and complexity in the mammalian kidney.

Cell reports·2026

Same author

Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.

Nature cardiovascular research·2026

Same author

A life course approach to preventing chronic kidney disease by limiting long-term consequences of early adverse events.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2026

Same author

Human pluripotent stem cell-derived macrophages modify development of human kidney organoids: a preliminary study.

Nephron·2026

Same author

Endurance exercise remodels pulmonary vein sleeve myocytes and promotes a proarrhythmic atrial substrate.

European heart journal·2026

Same author

Inhibition of calpain-mediated HMGB1 alleviates cardiac inflammation and dysfunction induced by ultra-processed foods.

JCI insight·2026

Search research articles

相关实验视频

Updated: Jul 19, 2025

Assessing Urinary Tract Junction Obstruction Defects by Methylene Blue Dye Injection

Assessing Urinary Tract Junction Obstruction Defects by Methylene Blue Dye Injection

Published on: October 12, 2017

使用机器学习预测先天性道形基因.

Mitra Kabir¹, Helen M Stuart^1,2, Filipa M Lopes³

¹CentreDivision of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.

Scientific reports

|August 14, 2023

概括

人工智能识别了与先天性道形 (RTMs) 相关的基因,这是儿童功能衰竭的主要原因. 这种方法有助于通过优先考虑候选基因进行遗传分析来诊断RTM.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

相关实验视频

Last Updated: Jul 19, 2025

Assessing Urinary Tract Junction Obstruction Defects by Methylene Blue Dye Injection

Assessing Urinary Tract Junction Obstruction Defects by Methylene Blue Dye Injection

Published on: October 12, 2017

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

科学领域:

遗传学是一个遗传学.
发展生物学发展生物学
生物信息学是一种生物信息学.

背景情况:

先天性道形 (RTMs) 是儿童严重功能衰竭的主要原因.
遗传因素与RTM有关,但在大多数情况下,致病变体仍未确定.
为基因分析优先考虑候选基因是RTM研究的一个重大挑战.

研究的目的:

开发和验证机器学习分类器,以识别参与发育的基因.
预测小鼠基因组中蛋白质编码基因与RTMs的关联状态.
加速对受影响儿童的RTM遗传诊断.

主要方法:

利用监督机器学习来识别发育基因共同的属性.
训练了一个分类器来预测可能参与发育的基因.
应用了验证的分类器来预测所有小鼠蛋白质编码基因的RTM关联.

主要成果:

成功验证了用于RTM基因识别的机器学习分类器.
产生了对小鼠基因组中所有蛋白质编码基因的RTM关联状态的预测.
该分类器有效地识别出高概率参与发育的基因.

结论:

机器学习为在RTM研究中优先考虑候选基因提供了一个强大的工具.
这些预测可以显著帮助RTMs的遗传诊断.
鉴定出的发育基因将加速对先天性缺陷的理解和诊断.