Random Forest for Bioinformatics
Random Forest for Bioinformatics
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DOI：
10.1007/978-1-4419-9326-7_11
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发表时间：
2012-01-01
期刊：
影响因子：
--
通讯作者：
Qi, Yanjun
中科院分区：
文献类型：
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作者：
Qi, Yanjun
现代生物学已经经历了越来越多地使用机器学习技术来进行大规模和复杂的生物数据分析。在生物信息学领域，随机森林(RF)[6]技术是一种流行的选择，它包括一组决策树，并在学习过程中自然地结合了特征选择和交互。它是非参数的、可解释的、高效的，并且对许多类型的数据具有很高的预测精度。由于射频在处理小样本、高维特征空间和复杂数据结构方面的独特优势，最近在计算生物学中的应用越来越多。
Modern biology has experienced an increased use of machine learning techniques for large scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest (RF) [6] technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice. It is nonparametric, interpretable, efficient, and has high prediction accuracy for many types of data. Recent work in computational biology has seen an increased use of RF, owing to its unique advantages in dealing with small sample size, high-dimensional feature space, and complex data structures.