计算机应用与软件2016，Vol.33Issue(5)：28-30,34,4.DOI:10.3969/j.issn.1000-386x.2016.05.008
基于直推式学习的视网膜致病基因预测模型
PRIORITISATION MODEL FOR RETINAL PATHOGENIC GENES BASED ON TRANSDUCTIVE LEARNING
摘要
Abstract
One of the major goals of biological science is to help people understand disease process,heritability and potential treatment in depth.However,it is usually a daunting job to discover the pathogenic genes,such as some inherited ocular diseases.On the basis of colligating numerous collected gene expression data,we presented a two-layer transductive machine learning (TTP)model used for finding potential retinal pathogenic genes.Its inner layer is in charge of gaining contribution degrees from multiple-dimensional features profile of Human BodyMap 2.0 and ocular tissues gene spectrum separately.In outer layer learning,the contribution degree obtained by inner layer will learn together with Crx and ChIP-Seq data to derive the prioritisation of the pathogenic genes.Experimental results showed that the transductive learning method did perform better than the traditional supervised learning method in accuracy on predicting pathogenic genes.In addition,an interesting finding was that the data integration was not always helpful.关键词
直推式学习/致病基因预测/机器学习/集成Key words
Transductive learning/Pathogenic genes prediction/Machine learning/Integration分类
信息技术与安全科学引用本文复制引用
董超,王小刚..基于直推式学习的视网膜致病基因预测模型[J].计算机应用与软件,2016,33(5):28-30,34,4.基金项目
国家自然科学基金项目（61472086）。 （）
