| Year |
Citation |
Score |
| 2022 |
Li M, Shi W, Zhang F, Zeng M, Li Y. A deep learning framework for predicting protein functions with co-occurrence of GO terms. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 35476573 DOI: 10.1109/TCBB.2022.3170719 |
0.351 |
|
| 2021 |
Li Y, Zeng M, Wu Y, Li Y, Li M. Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 34699365 DOI: 10.1109/TCBB.2021.3122294 |
0.377 |
|
| 2020 |
Zeng M, Lu C, Zhang F, Li Y, Wu FX, Li Y, Li M. SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning. Methods (San Diego, Calif.). PMID 32387314 DOI: 10.1016/J.Ymeth.2020.05.002 |
0.329 |
|
| 2020 |
Zeng M, Lu C, Fei Z, Wu F, Li Y, Wang J, Li M. DMFLDA: A deep learning framework for predicting IncRNA-disease associations. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 32248123 DOI: 10.1109/Tcbb.2020.2983958 |
0.323 |
|
| 2020 |
Luo H, Li M, Yang M, Wu FX, Li Y, Wang J. Biomedical data and computational models for drug repositioning: a comprehensive review. Briefings in Bioinformatics. PMID 32043521 DOI: 10.1093/Bib/Bbz176 |
0.319 |
|
| 2020 |
Zhang F, Song H, Zeng M, Wu F, Li Y, Pan Y, Li M. A deep learning framework for gene ontology annotations with sequence - and network-based information. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 31985440 DOI: 10.1109/Tcbb.2020.2968882 |
0.44 |
|
| 2019 |
Yang M, Luo H, Li Y, Wu FX, Wang J. Overlap matrix completion for predicting drug-associated indications. Plos Computational Biology. 15: e1007541. PMID 31869322 DOI: 10.1371/Journal.Pcbi.1007541 |
0.33 |
|
| 2019 |
Lu C, Yang M, Li M, Li Y, Wu F, Wang J. Predicting human lncRNA-disease associations based on geometric matrix completion. Ieee Journal of Biomedical and Health Informatics. PMID 31825885 DOI: 10.1109/Jbhi.2019.2958389 |
0.343 |
|
| 2019 |
Zeng M, Li M, Wu FX, Li Y, Pan Y. DeepEP: a deep learning framework for identifying essential proteins. Bmc Bioinformatics. 20: 506. PMID 31787076 DOI: 10.1186/S12859-019-3076-Y |
0.376 |
|
| 2019 |
Zeng M, Zhang F, Wu FX, Li Y, Wang J, Li M. Protein-protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics (Oxford, England). PMID 31593229 DOI: 10.1093/Bioinformatics/Btz699 |
0.43 |
|
| 2019 |
Li G, Li M, Peng W, Li Y, Pan Y, Wang J. A novel extended pareto optimality consensus model for predicting essential proteins. Journal of Theoretical Biology. PMID 31398315 DOI: 10.1016/J.Jtbi.2019.08.005 |
0.448 |
|
| 2019 |
Haratipour Z, Aldabagh H, Li Y, Greene LH. Network Connectivity, Centrality and Fragmentation in the Greek-Key Protein Topology. The Protein Journal. PMID 31317305 DOI: 10.1007/s10930-019-09850-7 |
0.336 |
|
| 2019 |
Li M, Wang Y, Zheng R, Shi X, Li Y, Wu F, Wang J. DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 31150344 DOI: 10.1109/Tcbb.2019.2919581 |
0.306 |
|
| 2019 |
Zhang F, Song H, Zeng M, Li Y, Kurgan L, Li M. DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions. Proteomics. e1900019. PMID 30941889 DOI: 10.1002/Pmic.201900019 |
0.445 |
|
| 2019 |
Zeng M, Li M, Fei Z, Wu F, Li Y, Pan Y, Wang J. A deep learning framework for identifying essential proteins by integrating multiple types of biological information. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 30736002 DOI: 10.1109/Tcbb.2019.2897679 |
0.387 |
|
| 2019 |
Chen X, Li M, Zheng R, Zhao S, Wu F, Li Y, Wang J. A novel method of gene regulatory network structure inference from gene knock-out expression data Tsinghua Science and Technology. 24: 446-455. DOI: 10.26599/Tst.2018.9010097 |
0.319 |
|
| 2019 |
Elhefnawy W, Li M, Wang J, Li Y. Decoding the Structural Keywords in Protein Structure Universe Journal of Computer Science and Technology. 34: 3-15. DOI: 10.1007/S11390-019-1895-Y |
0.439 |
|
| 2018 |
Li G, Li M, Wang J, Li Y, Pan Y. United neighborhood closeness centrality and orthology for predicting essential proteins. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 30596582 DOI: 10.1109/Tcbb.2018.2889978 |
0.465 |
|
| 2018 |
Li M, Fei Z, Zeng M, Wu F, Li Y, Pan Y, Wang J. Automated ICD-9 Coding via A Deep Learning Approach. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 29994157 DOI: 10.1109/Tcbb.2018.2817488 |
0.301 |
|
| 2017 |
Li M, Meng X, Zheng R, Wu FX, Li Y, Pan Y, Wang J. Identification of protein complexes by using a spatial and temporal active protein interaction network. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 28885159 DOI: 10.1109/Tcbb.2017.2749571 |
0.367 |
|
| 2016 |
Yaseen A, Nijim M, Williams B, Qian L, Li M, Wang J, Li Y. FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information. Bmc Bioinformatics. 17: 281. PMID 27587065 DOI: 10.1186/S12859-016-1117-3 |
0.691 |
|
| 2016 |
Zhao B, Wang J, Li M, Li X, Li Y, Wu F, Pan Y. A new method for predicting protein functions from dynamic weighted interactome networks. Ieee Transactions On Nanobioscience. PMID 26955047 DOI: 10.1109/Tnb.2016.2536161 |
0.442 |
|
| 2016 |
Lan W, Wang J, Li M, Liu J, Li Y, Wu FX, Pan Y. Predicting drug–target interaction using positive-unlabeled learning Neurocomputing. 206: 50-57. DOI: 10.1016/J.Neucom.2016.03.080 |
0.359 |
|
| 2016 |
Yaseen A, Ji H, Li Y. A load-balancing workload distribution scheme for three-body interaction computation on Graphics Processing Units (GPU) Journal of Parallel and Distributed Computing. 87: 91-101. DOI: 10.1016/J.Jpdc.2015.10.003 |
0.614 |
|
| 2016 |
Liang Y, Wu D, Liu G, Li Y, Gao C, Ma ZJ, Wu W. Big data-enabled multiscale serviceability analysis for aging bridges☆ Digital Communications and Networks. 2: 97-107. DOI: 10.1016/J.Dcan.2016.05.002 |
0.314 |
|
| 2015 |
Elhefnawy W, Chen L, Han Y, Li Y. ICOSA: A Distance-Dependent, Orientation-Specific Coarse-Grained Contact Potential for Protein Structure Modeling. Journal of Molecular Biology. 427: 2562-76. PMID 26055539 DOI: 10.1016/J.Jmb.2015.05.022 |
0.358 |
|
| 2014 |
Yaseen A, Li Y. Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features. Bmc Bioinformatics. 15: S3. PMID 25080939 DOI: 10.1186/1471-2105-15-S8-S3 |
0.689 |
|
| 2014 |
Yaseen A, Li Y. Context-based features enhance protein secondary structure prediction accuracy. Journal of Chemical Information and Modeling. 54: 992-1002. PMID 24571803 DOI: 10.1021/Ci400647U |
0.681 |
|
| 2013 |
Yaseen A, Li Y. Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy. Bmc Bioinformatics. 14: S9. PMID 24267383 DOI: 10.1186/1471-2105-14-S13-S9 |
0.681 |
|
| 2013 |
Li Y, Liu H, Rata I, Jakobsson E. Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment. Journal of Chemical Information and Modeling. 53: 500-8. PMID 23336295 DOI: 10.1021/Ci300207X |
0.463 |
|
| 2012 |
Yaseen A, Li Y. Accelerating knowledge-based energy evaluation in protein structure modeling with Graphics Processing Units Journal of Parallel and Distributed Computing. 72: 297-307. DOI: 10.1016/J.Jpdc.2011.10.005 |
0.676 |
|
| 2012 |
Liu H, Li Y, Rata I, Jakobsson E. A Next Step in Protein Secondary Structure Prediction Biophysical Journal. 102: 619a. DOI: 10.1016/J.Bpj.2011.11.3372 |
0.448 |
|
| 2011 |
Li Y, Rata I, Jakobsson E. Sampling multiple scoring functions can improve protein loop structure prediction accuracy. Journal of Chemical Information and Modeling. 51: 1656-66. PMID 21702492 DOI: 10.1021/Ci200143U |
0.41 |
|
| 2011 |
Zhu W, Yaseen A, Li Y. DEMCMC-GPU: An Efficient Multi-Objective Optimization Method with GPU Acceleration on the Fermi Architecture New Generation Computing. 29: 163-184. DOI: 10.1007/S00354-010-0103-Y |
0.623 |
|
| 2010 |
Li Y, Rata I, Chiu SW, Jakobsson E. Improving predicted protein loop structure ranking using a Pareto-optimality consensus method. Bmc Structural Biology. 10: 22. PMID 20642859 DOI: 10.1186/1472-6807-10-22 |
0.409 |
|
| 2010 |
Rata IA, Li Y, Jakobsson E. Backbone statistical potential from local sequence-structure interactions in protein loops. The Journal of Physical Chemistry. B. 114: 1859-69. PMID 20070091 DOI: 10.1021/Jp909874G |
0.404 |
|
| 2008 |
Li Y, Bordner AJ, Tian Y, Tao X, Gorin AA. Extensive exploration of conformational space improves Rosetta results for short protein domains. Computational Systems Bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference. 7: 203-9. PMID 19642281 |
0.303 |
|
| 2005 |
Li Y, Mascagni M. Grid-based quasi-Monte Carlo applications Monte Carlo Methods and Applications. 11: 39-55. DOI: 10.1515/1569396054027265 |
0.309 |
|
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