A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases X Chen, YA Huang, ZH You, GY Yan, XS Wang Bioinformatics 33 (5), 733-739, 2017 | 234 | 2017 |
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction X Chen, CC Yan, X Zhang, ZH You, YA Huang, GY Yan Oncotarget 7 (40), 65257, 2016 | 225 | 2016 |
Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding YA Huang, ZH You, X Chen, K Chan, X Luo BMC bioinformatics 17, 1-11, 2016 | 158 | 2016 |
A survey on computational models for predicting protein–protein interactions L Hu, X Wang, YA Huang, P Hu, ZH You Briefings in bioinformatics 22 (5), bbab036, 2021 | 132 | 2021 |
ILNCSIM: improved lncRNA functional similarity calculation model YA Huang, X Chen, ZH You, DS Huang, KCC Chan Oncotarget 7 (18), 25902, 2016 | 127 | 2016 |
An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network L Wang, ZH You, YA Huang, DS Huang, KCC Chan Bioinformatics 36 (13), 4038-4046, 2020 | 123 | 2020 |
Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein‐Protein Interactions from Protein Sequence YA Huang, ZH You, X Gao, L Wong, L Wang BioMed research international 2015 (1), 902198, 2015 | 123 | 2015 |
GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm L Wang, ZH You, YM Li, K Zheng, YA Huang PLOS Computational Biology 16 (5), e1007568, 2020 | 114 | 2020 |
Constructing prediction models from expression profiles for large scale lncRNA–miRNA interaction profiling YA Huang, KCC Chan, ZH You Bioinformatics 34 (5), 812-819, 2018 | 107 | 2018 |
FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model X Chen, YA Huang, XS Wang, ZH You, KCC Chan Oncotarget 7 (29), 45948, 2016 | 104 | 2016 |
Graph convolution for predicting associations between miRNA and drug resistance Y Huang, P Hu, KCC Chan, ZH You Bioinformatics 36 (3), 851-858, 2020 | 99 | 2020 |
Prediction of microbe–disease association from the integration of neighbor and graph with collaborative recommendation model YA Huang, ZH You, X Chen, ZA Huang, S Zhang, GY Yan Journal of translational medicine 15, 1-11, 2017 | 96 | 2017 |
A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences YA Huang, ZH You, X Chen Current Protein and Peptide Science 19 (5), 468-478, 2018 | 91 | 2018 |
Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor L Wong, ZH You, S Li, YA Huang, G Liu Advanced Intelligent Computing Theories and Applications: 11th International …, 2015 | 83 | 2015 |
iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation K Zheng, ZH You, JQ Li, L Wang, ZH Guo, YA Huang PLoS Computational Biology 16 (5), e1007872, 2020 | 73 | 2020 |
IMS-CDA: prediction of CircRNA-disease associations from the integration of multisource similarity information with deep stacked autoencoder model L Wang, ZH You, JQ Li, YA Huang IEEE transactions on cybernetics 51 (11), 5522-5531, 2020 | 62 | 2020 |
Plant species recognition methods using leaf image: Overview S Zhang, W Huang, Y Huang, C Zhang Neurocomputing 408, 246-272, 2020 | 60 | 2020 |
Detection of interactions between proteins through rotation forest and local phase quantization descriptors L Wong, ZH You, Z Ming, J Li, X Chen, YA Huang International journal of molecular sciences 17 (1), 21, 2015 | 52 | 2015 |
SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network HJ Jiang, YA Huang, ZH You Scientific reports 10 (1), 4972, 2020 | 51 | 2020 |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder YA Huang, ZA Huang, ZH You, Z Zhu, WZ Huang, JX Guo, CQ Yu Frontiers in genetics 10, 758, 2019 | 50 | 2019 |