Jiajie Peng
Jiajie Peng
Northwestern Polytechnical University;Harvard Medical School;Harbin Institute of Technology
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Cited by
Cited by
Identifying drug–target interactions based on graph convolutional network and deep neural network
T Zhao, Y Hu, LR Valsdottir, T Zang, J Peng
Briefings in bioinformatics 22 (2), 2141-2150, 2021
A learning-based framework for miRNA-disease association identification using neural networks
J Peng, W Hui, Q Li, B Chen, J Hao, Q Jiang, X Shang, Z Wei
Bioinformatics, 2019
SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association
L Cheng, J Li, P Ju, J Peng, Y Wang
PloS one 9 (6), e99415, 2014
LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data
Q Jiang, R Ma, J Wang, X Wu, S Jin, J Peng, R Tan, T Zhang, Y Li, ...
BMC genomics 16, 1-11, 2015
LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression
Q Jiang, J Wang, X Wu, R Ma, T Zhang, S Jin, Z Han, R Tan, J Peng, G Liu, ...
Nucleic acids research 43 (D1), D193-D196, 2015
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction
J Peng, Y Wang, J Guan, J Li, R Han, J Hao, Z Wei, X Shang
Briefings in bioinformatics 22 (5), bbaa430, 2021
DeepLGP: a novel deep learning method for prioritizing lncRNA target genes
T Zhao, Y Hu, J Peng, L Cheng
Bioinformatics 36 (16), 4466-4472, 2020
Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson’s disease
G Liu, J Peng, Z Liao, JJ Locascio, JC Corvol, F Zhu, X Dong, ...
Nature genetics 53 (6), 787-793, 2021
Predicting Parkinson's disease genes based on node2vec and autoencoder
J Peng, J Guan, X Shang
Frontiers in genetics 10, 226, 2019
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
J Peng, J Li, X Shang
BMC bioinformatics 21 (Suppl 13), 394, 2020
InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk
L Cheng, Y Jiang, H Ju, J Sun, J Peng, M Zhou, Y Hu
BMC genomics 19, 125-134, 2018
Integrating multi-network topology for gene function prediction using deep neural networks
J Peng, H Xue, Z Wei, I Tuncali, J Hao, X Shang
Briefings in bioinformatics 22 (2), 2096-2105, 2021
SC2disease: a manually curated database of single-cell transcriptome for human diseases
T Zhao, S Lyu, G Lu, L Juan, X Zeng, Z Wei, J Hao, J Peng
Nucleic Acids Research 49 (D1), D1413-D1419, 2021
Deep learning‐based classification and mutation prediction from histopathological images of hepatocellular carcinoma
H Liao, Y Long, R Han, W Wang, L Xu, M Liao, Z Zhang, Z Wu, X Shang, ...
Clinical and translational medicine 10 (2), 2020
Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data
J Peng, X Wang, X Shang
BMC bioinformatics 20, 1-12, 2019
Prediction and collection of protein–metabolite interactions
T Zhao, J Liu, X Zeng, W Wang, S Li, T Zang, J Peng, Y Yang
Briefings in bioinformatics 22 (5), bbab014, 2021
Identifying term relations cross different gene ontology categories
J Peng, H Wang, J Lu, W Hui, Y Wang, X Shang
BMC bioinformatics 18, 67-74, 2017
Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach
J Peng, X Zhang, W Hui, J Lu, Q Li, S Liu, X Shang
BMC systems biology 12, 109-116, 2018
A novel method to measure the semantic similarity of HPO terms
J Peng, H Xue, Y Shao, X Shang, Y Wang, J Chen
International Journal of Data Mining and Bioinformatics 17 (2), 173-188, 2017
Measuring semantic similarities by combining gene ontology annotations and gene co-function networks
J Peng, S Uygun, T Kim, Y Wang, SY Rhee, J Chen
BMC bioinformatics 16, 1-14, 2015
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