小柯机器人

通过推断慢性阻塞性肺病可识别新的遗传位点并改进风险模型
2023-04-23 21:42

美国谷歌健康的人工智能Farhad Hormozdiari和Justin Cosentino共同合作,近期取得重要工作进展。他们研究提出,通过对原始肺活量图的深度学习推断慢性阻塞性肺病可识别新的遗传位点并改进风险模型。相关研究成果2023年4月17日在线发表于《自然—遗传学》杂志上。

据介绍,慢性阻塞性肺病(COPD)是全球第三大死亡原因,具有高度遗传性。虽然COPD在临床上是通过将阈值应用于肺功能的汇总测量来定义的,但定量责任评分更能识别遗传信号。

研究人员在嘈杂的自我报告和国际疾病分类标签上训练一个深度卷积神经网络,从高维原始肺活量图中预测COPD病例控制状态,并使用模型的预测作为责任评分。基于机器学习(ML)的责任评分准确地区分了COPD病例和对照组,并在没有任何特定领域知识的情况下预测了COPD相关的住院治疗。

此外,基于ML的责任评分与总生存率和恶化事件相关。一项基于ML的责任评分的全基因组关联研究复制了现有的COPD和肺功能基因座,并确定了67个新的基因座。

总之,这一方案提供了一个使用ML方法和基于病历的标签的通用框架,这些方法不需要领域知识或专家管理来改进疾病预测和药物设计的基因组发现。

附:英文原文

Title: Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models

Author: Cosentino, Justin, Behsaz, Babak, Alipanahi, Babak, McCaw, Zachary R., Hill, Davin, Schwantes-An, Tae-Hwi, Lai, Dongbing, Carroll, Andrew, Hobbs, Brian D., Cho, Michael H., McLean, Cory Y., Hormozdiari, Farhad

Issue&Volume: 2023-04-17

Abstract: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case–control status from high-dimensional raw spirograms and use the model’s predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.

DOI: 10.1038/s41588-023-01372-4

Source: https://www.nature.com/articles/s41588-023-01372-4

Nature Genetics:《自然—遗传学》,创刊于1992年。隶属于施普林格·自然出版集团,最新IF:41.307
官方网址:https://www.nature.com/ng/
投稿链接:https://mts-ng.nature.com/cgi-bin/main.plex


本期文章:《自然—遗传学》:Online/在线发表

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