使用机器学习和miRNA来诊断食道癌症
Vishnu A Aravind1, Valentina L Kouznetsova2,3,4, Santosh Kesari5
1REHS program, San Diego Supercomputer Center, UC San Diego, San Diego, CA, United States.
The journal of applied laboratory medicine
|May 9, 2024
概括
机器学习模型显示出使用microRNAs (miRNAs) 诊断食道癌症 (EC) 的前景. 纯粹的贝叶斯实现了0.94准确度,突出了miRNAs作为早期EC检测的潜在的非侵入性生物标志物.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 食道癌 (EC) 是全球健康负担很大,由于晚期诊断,死亡率很高.
- 目前用于EC的诊断方法缺乏足够的疗效.
- 微RNAs (miRNAs) 提供了作为EC检测的新型非侵入性生物标志物的潜力.
研究的目的:
- 为了评估miRNAs对食道癌的诊断准确性.
- 使用机器学习区分与EC相关的miRNA和控制miRNA.
主要方法:
- 应用了机器学习 (ML) 算法,包括 naive Bayes,多层感知子,Hoeffding树,随机森林和随机树.
- 在InfoGain的特征选择中,确定了关键的miRNA序列和基因标描述符.
- 模型在miRNA数据集上使用WEKA和TensorFlow Keras进行训练和验证.
主要成果:
- 性能最好的WEKA分类器是随机森林,霍夫丁树和朴素的贝叶斯.
- 该TensorFlow Keras模型实现了0.91.1的精度.
- 最好的WEKA模型,纯粹的贝叶斯模型,显示出0.94.9的高精度.
结论:
- 基于ML的miRNA分类器显示出诊断EC的巨大潜力.
- 需要进一步的研究来验证这些发现并评估临床效用.
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