拓嵌入和定向特征在集体分类器中的重要性,用于多类分类的分类
Eloisa Rocha Liedl1,2, Shabeer Mohamed Yassin1,3, Melpomeni Kasapi1,4
1Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Hospital Campus, Imperial College London, London, W12 0NN, United Kingdom.
Computational and structural biotechnology journal
|December 3, 2024
概括
我们开发了一种新的基于类的定向特征重要性 (CLIFI) 度量,以提高机器学习在癌症生物标志物发现中的解释性. 这种方法增强了对各种癌症类型中蛋白质表达模式的理解.
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
- 机器学习在瘤学中
背景情况:
- 癌症仍然是全球主要的死亡原因,需要在早期检测和治疗方面取得进展.
- 机器学习 (ML) 提供了从高维数据中识别新型癌症生物标志物的潜力.
- 解释ML模型具有挑战性,但对于生物学洞察力和临床应用至关重要.
研究的目的:
- 为基于决策树的ML模型开发和评估一种新的基于类的定向特征重要性 (CLIFI) 度量.
- 提高用于癌症生物标志物识别的ML模型的可解释性.
- 对癌症分类的癌症基因组图谱 (TCGA) 蛋白质组学数据进行CLIFI集成模型的性能评估.
主要方法:
- 开发了决策树方法的CLIFI指标,并将其集成到随机森林 (RF),LAtent变量静态树集 (LAVASET),梯度增强决策树 (GBDTs) 和一个新的LAVABOOST扩展中.
- 将蛋白质与蛋白质相互作用网络拓纳入LAVASET和LAVABOOST模型.
- 将模型应用于TCGA蛋白质组学数据,用于分类28种癌症类型.
主要成果:
- CLIFI指标促进了对ML模型决策过程的可视化.
- 模型获得高F1分数:RF (92.6%),LAVASET (92.0%),LAVABOOST (89.3%) 和GBDT (85.7%),没有一个模型在所有癌症中都优越.
- CLIFI分析揭示了MYH11,ERα和BCL2等蛋白质在各种癌症类型 (例如大脑,乳腺,脏,前列腺) 的异质表达模式.
结论:
- CLIFI指标提供了一种综合方法,用于在多类分类中对可解释特征的重要性进行评估.
- 将CLIFI与拓信息相结合,引入了感应偏差,提高了模型的可解释性.
- 这种方法有助于理解癌症中的复杂蛋白质表达异质性,支持生物标志物发现.
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