Yanhui Li
Peking University
18 Papers
31 Citations
Yanhui Li is an academic researcher from Peking University. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 10, co-authored 18 publications. Previous affiliations of Yanhui Li include University of Electronic Science and Technology of China & Harbin Medical University.
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Papers
SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features
TL;DR: A computational predictor of mammalian m(6)A site named SRAMP, which combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively.
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Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network
Zheng Guo,Yongjin Li,Xue Gong,Chen Yao,Wencai Ma,Dong Wang,Yanhui Li,Jing Zhu,Min Zhang,Da Yang,Jing Wang +10 more
TL;DR: A novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles and suggests a systematic approach to evaluate the biological relevance of the identified responsive sub- network by its ability of capturing condition-relevant functional modules.
ViRBase: a resource for virus–host ncRNA-associated interactions
Yanhui Li,Changliang Wang,Zhengqiang Miao,Xiaoman Bi,Deng Wu,Nana Jin,Liqiang Wang,Hao Wu,Kun Qian,Chunhua Li,Ting Zhang,Chunrui Zhang,Ying Yi,Hongyan Lai,Yongfei Hu,Lixin Cheng,Kwong-Sak Leung,Xiaobo Li,Fengmin Zhang,Kongning Li,Xia Li,Dong Wang +21 more
TL;DR: The current version of ViRBase documents more than 12 000 viral and cellular ncRNA-associated virus–virus, virus–host, host-virus and host–host interactions involving more than 460 non-redundant ncRNAs and 4400 protein-coding genes from between more than 60 viruses and 20 hosts.
Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules
Dong Wang,Yingli Lv,Zheng Guo,Xia Li,Yanhui Li,Jing Zhu,Da Yang,Jianzhen Xu,Chenguang Wang,Shaoqi Rao,Baofeng Yang +10 more
TL;DR: Among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods, and the recently proposed functional expression profile (FEP) approach is demonstrated as a means to handle microarray data with MVs.
Globally predicting protein functions based on co-expressed protein-protein interaction networks and ontology taxonomy similarities
TL;DR: This paper introduces gene expression data to filter the interacting neighbors of a protein in order to enhance the degree of functional consensus among the neighbors and shows that the proposed GESTs method is powerful for predicting protein function to very specific terms.
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