医学
生命银行
一致性
外显子组测序
乳腺癌
外显子组
肿瘤科
内科学
癌症
机器学习
突变
算法
人工智能
生物信息学
基因
遗传学
计算机科学
生物
作者
Bum Sup Jang,In Young Kim
出处
期刊:Biomarkers in Medicine
[Future Medicine]
日期:2021-11-01
卷期号:15 (16): 1529-1539
被引量:3
标识
DOI:10.2217/bmm-2021-0280
摘要
Aim: We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Materials & methods: Whole-exome sequencing data derived from 1181 female breast cancer patients within the UK Biobank was collected. We found feature genes (n = 50) regarding total mutation burden using the long short-term memory model. Then, we developed the XGBoost survival model with selected feature genes. Results: The XGBoost survival model performed acceptably, with a concordance index of 0.75 and a scaled Brier score of 0.146 in terms of overall survival prediction. The high-mutation group exhibited inferior overall survival compared with the low-mutation group in patients ≥56 years (log-rank test, p = 0.042). Conclusion: We showed that machine-learning algorithms can be used to predict overall survival in breast cancer patients from blood-based whole-exome sequencing data.
科研通智能强力驱动
Strongly Powered by AbleSci AI