Novel data fusion method for candidate gene prioritization
一种新的用于候选基因排序的数据融合方法

Identifying key candidates in the thousands of genes in a genome is an important step in hunting genes playing roles in a disease phenotype or a complex biological process, and candidate gene prioritization integrating kinds of data sources is becoming a new challenge in this field. A new data fusion method based on one-class Support Vector Machine (SVM) was proposed for candidate gene prioritization. Experimental results indicate that the proposed method is valid in gene prioritization integrating kinds of heterogeneous data sources and its accuracy and robustness are better than that of the method with single data source.