瘤类型的分类和候选癌症特异性生物标志物的发现通过半监督学习
Peng Chen1, Zhenlei Li1, Zhaolin Hong1
1School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
Biophysics reports
|September 27, 2023
概括
一个新的深度学习模型,MSSL,通过整合多个数据集,准确地分类瘤类型并发现癌症生物标志物. 这种方法克服了数据的局限性,并提高了各种癌症的诊断和预后能力.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 识别与癌症相关的差异表达基因对于诊断,预后和治疗至关重要.
- 深度学习 (DL) 方法在基因差异表达分析方面表现有前途,但在有限的数据和数据异质性方面面临挑战.
- 现有的DL模型与小数据集,不同机构的数据质量不一致以及罕见癌症的代表性不足而扎.
研究的目的:
- 开发一个强大的基于多个数据集的半监督学习 (MSSL) 模型,用于瘤类型分类和癌症特异性生物标志物发现.
- 解决单一数据集方法的局限性,并有效地利用多样化,多机构的基因表达数据集.
- 为了改善DL性能,对于常见和罕见的癌症类型.
主要方法:
- 提出了一种用于多数据集集成的新型半监督学习模型 (MSSL).
- 应用MSSL到癌症基因组图谱 (TCGA) 和基因表达总汇 (GEO) 全癌症规范化RNA-seq数据.
- 利用分类结果对每个癌症类型的基因重要性进行排名.
主要成果:
- 获得了97.6%的最终分类准确度,比以前的方法显著提高了性能.
- 根据基因重要性确定并排列候选癌症特异性生物标志物.
- 验证了顶级基因对各种瘤的生物学意义和潜在生物标志物实用性.
结论:
- MSSL模型为多数据集瘤分类和生物标志物发现提供了强大而有效的方法.
- 这种方法克服了将DL应用于异质和有限的癌症基因组学数据的关键挑战.
- 这些已识别的基因有望成为癌症诊断和预后的临床相关生物标志物.
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