用降维与可解释AI从海量基因数据中精准识别33种癌症及关键生物标志物
Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI
- 通过特征选择将19,238维基因数据压缩至500维，保留核心信息
- 集成三种最优分类器实现96.61%的癌症分类准确率
- 结合差异表达分析揭示关键基因的生物学意义，适合精准医疗研究者
基因表达分析是癌症分类的关键方法，可通过识别与各类肿瘤相关的独特分子特征实现精确诊断。从基因表达值中识别出癌症特异性基因，有助于实现更个性化的治疗方案。然而，mRNA基因表达数据的高维度给分析和信息提取带来了挑战。本研究提出一个综合流程，旨在准确识别33种不同癌症类型及其对应的基因集合。该流程结合了归一化与特征选择技术，有效降低数据维度的同时保持高性能。值得注意的是，我们的流程仅使用500个特征就成功识别出大量癌症特异性基因，远低于全数据集19,238个特征的规模。通过集成三种表现最佳的分类器，实现了96.61%的分类准确率。此外，我们利用可解释AI阐明所识别癌症特异性基因的生物学意义，采用差异基因表达（DGE）分析进行验证。
原文摘要 · Abstract (English)
Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific genes from gene expression values enables a more tailored and personalized treatment approach. However, the high dimensionality of mRNA gene expression data poses challenges for analysis and data extraction. This research presents a comprehensive pipeline designed to accurately identify 33 distinct cancer types and their corresponding gene sets. It incorporates a combination of normalization and feature selection techniques to reduce dataset dimensionality effectively while ensuring high performance. Notably, our pipeline successfully identifies a substantial number of cancer-specific genes using a reduced feature set of just 500, in contrast to using the full dataset comprising 19,238 features. By employing an ensemble approach that combines three top-performing classifiers, a classification accuracy of 96.61% was achieved. Furthermore, we leverage Explainable AI to elucidate the biological significance of the identified cancer-specific genes, employing Differential Gene Expression (DGE) analysis.
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