Machine Learning and Ensemble Learning for Transcriptome Data: Principles and Advances

Abstract

Nowadays, as the next-generation RNA-seq sequencing technology and machine learning algorithms continue to advance, an increasing number of machine learning methods are being used in plant transcriptome research. Because of its high robustness, good generalization performance and strong interpretability, the ensemble learning framework in machine learning outperforms classic linear statistical methods in the classification and prediction of plant attributes, gene importance evaluation, and molecular breeding. To begin, this article will focus on ensemble learning’s essential ideas and frontier models. Additionally, the advancement of RNA-seq technology and the establishment of databases for transcriptome research would be discussed. Furthermore, cutting-edge machine learning research in plant genome and transcriptome analysis will be given, together with the innovation points, benefits, and limitations of each machine learning model algorithm and transcriptome technology. The article establishes a framework for the integration of artificial intelligence and plant bioinformatics on an interdisciplinary and in-depth level.