一个预测瘤基因组进展的框架,帮助个性化治疗胃癌
Fahad M Alotaibi1, Yaser Daanial Khan2
1Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|July 14, 2023
概括
这项研究使用像LSTM,Bi-LSTM和GRU这样的深度学习模型来识别胃癌基因突变. 这些先进的方法提高了早期检测和癌症进展的理解.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 基因突变是诊断癌症的关键指标,包括胃癌.
- 现有的用于突变检测的机器和深度学习模型需要优化,因为总化的数据集限制.
- 深度学习提供计算能力来分析胃癌驱动的复杂基因突变.
研究的目的:
- 提出和评估用于识别胃癌进展的深度学习模型.
- 提高检测与胃癌相关的基因突变的准确性和效率.
- 为了优化分析,使用长短期内存 (LSTM),门式循环单元 (GRU) 和Bi-LSTM.
主要方法:
- 利用了来自 intOGen.org.org 的 61 个致癌驱动基因的突变数据.
- 包含来自asia.ensembl.org的正常基因序列用于模型培训.
- 使用自我一致性测试 (SCT),十倍交叉验证测试 (FCVT) 和独立集合测试 (IST) 验证的深度学习模型.
主要成果:
- 提出的深度学习模型在独立集合测试 (IST) 上实现了高预测指标.
- 双LSTM和GRU模型表现出优异的性能,具有100%的特异性和1.00的AUC.
- LSTM,Bi-LSTM和GRU模型显示高精度 (97.18%-99.46%) 和灵敏度 (98.35%-98.93%).
结论:
- 深度学习方法,特别是LSTM,Bi-LSTM和GRU,对于识别胃癌进展是有效的.
- 提出的模型显示了优化检测胃癌中的基因突变的显著潜力.
- 这些发现有助于通过计算方法推进胃癌的早期诊断能力.
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