亚型
支持向量机
特征选择
人工智能
分级(工程)
分类器(UML)
少突胶质瘤
机器学习
模式识别(心理学)
计算机科学
相关性
生物
胶质瘤
星形细胞瘤
数学
癌症研究
生态学
程序设计语言
几何学
作者
Sana Munquad,Tapas Si,Saurav Mallik,Aimin Li,Asim Bikas Das
出处
期刊:Briefings in Functional Genomics
[Oxford University Press]
日期:2022-08-03
卷期号:21 (5): 408-421
被引量:2
摘要
Abstract Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.
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