J
Jiajie Peng
Researcher at Northwestern Polytechnical University
Publications - 101
Citations - 2220
Jiajie Peng is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Semantic similarity. The author has an hindex of 21, co-authored 84 publications receiving 1402 citations. Previous affiliations of Jiajie Peng include Brigham and Women's Hospital & Michigan State University.
Papers
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Identifying drug–target interactions based on graph convolutional network and deep neural network
TL;DR: A novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification that first uses a graph convolved network to learn the features for each DPP, and uses a deep neural network to predict the final label.
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LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data
Qinghua Jiang,Rui Ma,Jixuan Wang,Xiaoliang Wu,Shuilin Jin,Jiajie Peng,Renjie Tan,Tianjiao Zhang,Yu Li,Yadong Wang +9 more
TL;DR: The hypergeometric test is used to functionally annotate a single lnc RNA or a set of lncRNAs with significantly enriched functional terms among the protein-coding genes that are significantly co-expressed with the lncRNA(s).
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LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression
Qinghua Jiang,Jixuan Wang,Xiaoliang Wu,Rui Ma,Tianjiao Zhang,Shuilin Jin,Zhijie Han,Renjie Tan,Jiajie Peng,Guiyou Liu,Yu Li,Yadong Wang +11 more
TL;DR: A curated database named LncRNA2Target, which stores lncRNA-to-target genes and is publicly accessible at http://www.lncrna2target.org, which provides a web interface through which its users can search for the targets of a particular lnc RNA or for the lncRNAs that target a particular gene.
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SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association.
TL;DR: The high average AUC (area under the receiver operating characteristic curve) shows that SemFunSim is an effective method for drug repositioning, and when using the method on diseases without annotated compounds in CTD, it could confirm many of the predicted candidate compounds from literature.
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A learning-based framework for miRNA-disease association identification using neural networks.
Jiajie Peng,Weiwei Hui,Qianqian Li,Bolin Chen,Jianye Hao,Qinghua Jiang,Xuequn Shang,Zhongyu Wei +7 more
TL;DR: A novel learning-based framework, MDA-CNN, is proposed, which outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and mi RNA-phenotype association prediction.