基于半监督学习的黄萎病互作基因对的预测

Abstract

黄萎病(Greensickness)属于不可治愈性病害，每年均造成巨大的经济损失。为了培育抗病植株、得到更多的互作基因对，逐次进行生物实验排除是不现实的。为了在已知少量关联基因的情况下挖掘更多的可靠基因对，本文主要使用统计技术典型相关分析(Canonical Correlation Analysis, CCA)和数据挖掘技术半监督学习(Semi-Supervised Learning, SSL)等生物信息技术对相关基因进行学习，最终实现对关联基因的预测。研究结果能够有效地指导黄萎病抗病研究的方向、精确研究范围、提高研究速度。 Greensickness is an incurable disease, which can cause huge economic losses every year. Using a series of biological experiments to develop resistant plants and get more interaction gene is un-realistic. In order to dig more reliable genes under the premise of only having very few interaction gene, the paper mainly uses bio-IT, such as statistical techniques of Canonical Correlation Analysis and data mining techniques of Semi-Supervised Learning to study the related gene and gets the prediction of the interaction gene at the end. The result of the study can effectively guide the direction of the greensickness research, narrow the scope of the research and improve the research speed.