Current research status of machine learning methods on predicting plant genomic information
With the rapid development of high-throughput sequencing technology,massive multi-omics data has been accumulated in plant genomics research.Therefore,how to develop and improve the relevant processing software tools,so as to effectively use these massive data to explore useful biological information,has become an important scientific problem that needs to be solved.Among them,machine learning method has been widely concerned in this field because of its remarkable ability of prediction,classification,data mining and integration.We reviewed the research progresses of machine learning in plant genome on function prediction,focusing on the application results of machine learning model in plant molecular interaction prediction,important functional site prediction,functional annotation,crop breeding and so on.We also look forward to the future development directions and application prospects in this field.This paper will help for plant researchers to quickly understand and apply machine learning methods,so as to contribute to the studies of plant genetic mechanism research and the improvement of crop traits.
machine learningplant genome predictionprotein-protein interactionmolecular site predictionfunctional annotationcrop breeding
NETL
NSTL
万方数据
维普
