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Table of Contents
1. Introduction
2. Random Forest and Extensions in Bioinformatics
- 2.1 Classification purpose
- 2.2 Measuring feature importance
- 2.3 Random forest proximity
3. Bioinformatic Applications of Random Forest and Variants
- 3.1 Analysis of microarray gene expression data
Summary
Modern biology has experienced an increasing use of machine learning techniques for large scale and complex biological data analysis. The Random Forest (RF) technique is a popular choice in Bioinformatics, providing accurate predictions and model interpretability. This chapter reviews notable extensions of random forest in bioinformatics, focusing on classification purposes, measuring feature importance, and random forest proximity. The applications of random forest in biological tasks include classifying different types of samples, identifying disease-associated genes, recognizing important elements in protein sequences, and identifying protein-protein interactions. Random forest and its variants have been successfully applied to various bioinformatic problems, such as gene expression classification, mass spectrum protein expression analysis, biomarker discovery, sequence annotation, and protein-protein interaction prediction.