基于个性化随机游走的基因-表型关联分析OA北大核心CSTPCD

Association analysis between genes and phenotypes is crucial to reveal the inherent genetic association of organisms.Random walk-based algorithms can fuse multiple omics data,aggregate the label information of first-order or higher-order neighbors,complete the association information between different nodes in the network,improve the accuracy of association prediction and further discover the potential genetic associations between genes and phenotypes.However,existing random walk algorithms usually treat each node equally and ignore the varying importance of different nodes,as such non-important nodes can be excessively propagated and the model performance is compromised.To this end,an indi-vidual multiple random walks(iMRW)algorithm based on multi-omics data fusion is proposed.On the heterogeneous ge-netic network composed with genes,miRNAs and phenotype nodes,we design the individual multiple random walks strate-gy based on the network topology,assign nodes of different importance with different walking lengths.We then complete the genetic information of different nodes by fusing multi-source association matrix,Gaussian interaction profile kernel sim-ilarity and random walk,and accurately obtain the gene-phenotype association prediction matrix.Under different experi-mental settings,iMRW can achieve the best prediction performance compared with the state-of-the-art algorithms.The case study with respect to maize photosynthetic ability and starch content further confirm the usefulness and effectiveness of iMRW in identifying potential gene-phenotype associations.