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德尔-甲状腺:通过基因组突变检测甲状腺癌进展的深度集体学习框架

Asghar Ali Shah¹, Ali Daud², Amal Bukhari³

¹Center of Excellence in Artificial Intelligence (CoE-AI), Department of Computer Science, Bahria University, Islamabad, 04408, Pakistan.

BMC medical informatics and decision making

|July 22, 2024

概括

这项研究引入了一个集体深度学习模型,用于早期检测甲状腺癌突变. 该模型达到96%的准确性,为识别与癌症相关的遗传变化提供了一个有希望的工具.

科学领域:

基因组学和生物信息学
计算生物学计算生物学
机器学习在瘤学中

背景情况:

基因突变是癌症发展的关键因素.
深度学习模型显示了识别癌症相关突变的巨大潜力.
甲状腺癌是美国普遍存在的恶性瘤,需要早期检测方法.

研究的目的:

开发和评估一个集体学习模型,用于早期检测甲状腺癌突变.
利用深度学习技术,包括长期短期记忆 (LSTM),门式循环单元 (GRU) 和双向LSTM (Bi-LSTM).

主要方法:

开发了一个集体深度学习模型,集成LSTM,GRUs和Bi-LSTM.
该模型是通过来自asia.ensembl.org和IntOGen.org的41个基因中的969个突变的633个样本的数据集进行训练的.
特征提取涉及哈恩时刻,中心时刻,原始时刻和基于矩阵的方法,使用自我一致性,独立集和10倍交叉验证测试进行评估.

主要成果:

组合学习模型在独立集合测试 (IST) 中实现了96%的准确性.
综合统计评估包括准确性,回忆,灵敏度,特异性,MCC,损失和F1评分等指标.

结论:

拟议的集体深度学习模型在检测甲状腺癌突变方面表现出很高的性能.
这种方法为早期识别与甲状腺癌相关的遗传变异提供了一个有希望的策略.

关键词:

双向LSTM (双向LSTM) 是一种双向的LSTM.深度学习是一种深度学习.集体学习模型 (ELM)门式经常性单位 (GRU)长时间的短期记忆 (LSTM)突变检测检测突变检测检测甲状腺癌甲状腺癌是什么?

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