| 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 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2018 |
Ni P, Wang J, Zhong P, Li Y, Wu F, Pan Y. Constructing Disease Similarity Networks Based on Disease Module Theory. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 29993782 DOI: 10.1109/Tcbb.2018.2817624 |
0.297 |
|
| 2021 |
Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical data and deep learning computational models for predicting compound-protein relations. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 33769935 DOI: 10.1109/TCBB.2021.3069040 |
0.297 |
|
| 2001 |
Mascagni M, Karaivanova A, Li Y. A Quasi-Monte Carlo Method for Elliptic Boundary Value Problems Monte Carlo Methods and Applications. 7. DOI: 10.1515/Mcma.2001.7.3-4.283 |
0.295 |
|
| 2018 |
Lu C, Yang M, Luo F, Wu FX, Li M, Pan Y, Li Y, Wang J. Prediction of lncRNA-disease associations based on inductive matrix completion. Bioinformatics (Oxford, England). PMID 29718113 DOI: 10.1093/Bioinformatics/Bty327 |
0.294 |
|
| 2017 |
Abdelrasoul M, Ponniah K, Mao A, Warden MS, Elhefnawy W, Li Y, Pascal SM. Conformational Clusters of Phosphorylated Tyrosine. Journal of the American Chemical Society. PMID 29121470 DOI: 10.1021/Jacs.7B10367 |
0.293 |
|
| 2003 |
Zhang Y, Peters MH, Li Y. Nonequilibrium, multiple-timescale simulations of ligand-receptor interactions in structured protein systems. Proteins. 52: 339-48. PMID 12866048 DOI: 10.1002/prot.10411 |
0.292 |
|
| 2016 |
He J, Li Y, Zelikovsk A. Guest Editorial: Introduction to the Special Issue on Bioinformatics Research and Applications Ieee Transactions On Nanobioscience. 16: 79-80. DOI: 10.1109/Tnb.2017.2678218 |
0.291 |
|
| 2020 |
Xuan W, Liu N, Huang N, Li Y, Wang J. CLPred: a sequence-based protein crystallization predictor using BLSTM neural network. Bioinformatics (Oxford, England). 36: i709-i717. PMID 33381840 DOI: 10.1093/bioinformatics/btaa791 |
0.29 |
|
| 2004 |
Li Y, Protopopescu VA, Gorin A. Accelerated simulated tempering Physics Letters A. 328: 274-283. DOI: 10.1016/J.Physleta.2004.05.067 |
0.285 |
|
| 2018 |
Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational Drug Repositioning using Low-Rank Matrix Approximation and Randomized Algorithms. Bioinformatics (Oxford, England). PMID 29365057 DOI: 10.1093/Bioinformatics/Bty013 |
0.279 |
|
| 2009 |
Li Y, Protopopescu VA, Arnold N, Zhang X, Gorin A. Hybrid parallel tempering and simulated annealing method Applied Mathematics and Computation. 212: 216-228. DOI: 10.1016/J.Amc.2009.02.023 |
0.279 |
|
| 2017 |
Ji H, Li Y. Block Conjugate Gradient algorithms for least squares problems Journal of Computational and Applied Mathematics. 317: 203-217. DOI: 10.1016/J.Cam.2016.11.031 |
0.278 |
|
| 2017 |
Li M, Zheng R, Li Y, Wu FX, Wang J. MGT-SM: A Method for Constructing Cellular Signal Transduction Networks. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 28541220 DOI: 10.1109/Tcbb.2017.2705143 |
0.277 |
|
| 2016 |
Ji H, Li Y. A breakdown-free block conjugate gradient method Bit Numerical Mathematics. 57: 379-403. DOI: 10.1007/S10543-016-0631-Z |
0.275 |
|
| 2022 |
Zhao Q, Duan G, Zhao H, Zheng K, Li Y, Wang J. GIFDTI: Prediction of drug-target interactions based on global molecular and intermolecular interaction representation learning. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 36445997 DOI: 10.1109/TCBB.2022.3225423 |
0.275 |
|
| 2022 |
Li M, Lu Z, Wu Y, Li Y. BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. Bioinformatics (Oxford, England). PMID 35043942 DOI: 10.1093/bioinformatics/btac035 |
0.274 |
|
| 2018 |
Yu W, Gu Y, Li Y. Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation Siam Journal On Matrix Analysis and Applications. 39: 1339-1359. DOI: 10.1137/17M1141977 |
0.273 |
|
| 2016 |
López-Blanco JR, Canosa-Valls AJ, Li Y, Chacón P. RCD+: Fast loop modeling server. Nucleic Acids Research. PMID 27151199 DOI: 10.1093/Nar/Gkw395 |
0.271 |
|
| 2020 |
Jiang H, Yang M, Chen X, Li M, Li Y, Wang J. miRTMC: A miRNA target prediction method based on matrix completion algorithm. Ieee Journal of Biomedical and Health Informatics. PMID 32287029 DOI: 10.1109/Jbhi.2020.2987034 |
0.264 |
|
| 2021 |
Wang K, Zhou R, Li Y, Li M. DeepDTAF: a deep learning method to predict protein-ligand binding affinity. Briefings in Bioinformatics. PMID 33834190 DOI: 10.1093/bib/bbab072 |
0.263 |
|
| 2003 |
Li Y, Mascagni M, Peters MH. Grid-based nonequilibrium multiple-time scale molecular dynamics/Brownian dynamics simulations of ligand-receptor interactions in structured protein systems Proceedings - Ccgrid 2003: 3rd Ieee/Acm International Symposium On Cluster Computing and the Grid. 568-573. DOI: 10.1109/CCGRID.2003.1199415 |
0.262 |
|
| 2007 |
Li Y, Song Y. An adaptive and trustworthy software testing framework on the grid The Journal of Supercomputing. 46: 124-138. DOI: 10.1007/S11227-007-0160-2 |
0.261 |
|
| 2019 |
Guo D, Duan G, Yu Y, Li Y, Wu FX, Li M. A Disease Inference Method Based on Symptom Extraction and Bidirectional Long Short Term Memory networks. Methods (San Diego, Calif.). PMID 31301375 DOI: 10.1016/J.Ymeth.2019.07.009 |
0.26 |
|
| 2020 |
Elhefnawy W, Li M, Wang J, Li Y. DeepFrag-k: a fragment-based deep learning approach for protein fold recognition. Bmc Bioinformatics. 21: 203. PMID 33203392 DOI: 10.1186/s12859-020-3504-z |
0.259 |
|
| 2017 |
Feng X, Li K, Yu W, Li Y. Fast Matrix Completion Algorithm Based on Randomized Singular Value Decomposition and its Applications Journal of Computer-Aided Design & Computer Graphics. 29: 2343. DOI: 10.3724/Sp.J.1089.2017.16603 |
0.258 |
|
| 2019 |
Yang M, Luo H, Li Y, Wang J. Drug repositioning based on bounded nuclear norm regularization. Bioinformatics (Oxford, England). 35: i455-i463. PMID 31510658 DOI: 10.1093/Bioinformatics/Btz331 |
0.255 |
|
| 2022 |
Zhao Q, Duan G, Yang M, Cheng Z, Li Y, Wang J. AttentionDTA: drug-target binding affinity prediction by sequence-based deep learning with attention mechanism. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 35471889 DOI: 10.1109/TCBB.2022.3170365 |
0.252 |
|
| 2013 |
Li Y. Conformational sampling in template-free protein loop structure modeling: an overview. Computational and Structural Biotechnology Journal. 5: e201302003. PMID 24688696 DOI: 10.5936/csbj.201302003 |
0.251 |
|
| 2010 |
Li Y, Rata I, Jakobsson E. Integrating multiple scoring functions to improve protein loop structure conformation space sampling 2010 Ieee Symposium On Computational Intelligence in Bioinformatics and Computational Biology, Cibcb 2010. 37-44. DOI: 10.1109/CIBCB.2010.5510687 |
0.242 |
|
| 2017 |
Hung J, Wang MC, Wang S, Abdelrasoul M, Li Y, He W. Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach Ieee Transactions On Emerging Topics in Computing. 5: 45-55. DOI: 10.1109/Tetc.2015.2504239 |
0.236 |
|
| 2022 |
Zhang L, Lu C, Zeng M, Li Y, Wang J. CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features. Briefings in Bioinformatics. PMID 36511222 DOI: 10.1093/bib/bbac530 |
0.235 |
|
| 2015 |
Ji H, Li Y, Weinberg S. Calcium Ion Fluctuations Alter Channel Gating in a Stochastic Luminal Calcium Release Site Model. Ieee/Acm Transactions On Computational Biology and Bioinformatics / Ieee, Acm. PMID 26561478 DOI: 10.1109/Tcbb.2015.2498552 |
0.235 |
|
| 2013 |
Ji H, Mascagni M, Li Y. Convergence analysis of Markov chain Monte Carlo linear solvers using Ulam-von Neumann algorithm Siam Journal On Numerical Analysis. 51: 2107-2122. DOI: 10.1137/130904867 |
0.235 |
|
| 2017 |
Liang Y, Xing X, Li Y. A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions Journal of Computational Physics. 338: 252-268. DOI: 10.1016/J.Jcp.2017.02.069 |
0.209 |
|
| 2021 |
Zhao H, Zheng K, Li Y, Wang J. A novel graph attention model for predicting frequencies of drug-side effects from multi-view data. Briefings in Bioinformatics. PMID 34213525 DOI: 10.1093/bib/bbab239 |
0.196 |
|
| 2023 |
Niu K, Wu Y, Li Y, Li M. Retrieve and rerank for automated ICD coding via Contrastive Learning. Journal of Biomedical Informatics. 104396. PMID 37211195 DOI: 10.1016/j.jbi.2023.104396 |
0.193 |
|
| 2021 |
Zhao H, Duan G, Ni P, Yan C, Li Y, Wang J. RNPredATC: a deep residual learning-based model with applications to the prediction of drug-ATC code association. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 34110998 DOI: 10.1109/TCBB.2021.3088256 |
0.193 |
|
| 2020 |
Yang M, Wu G, Zhao Q, Li Y, Wang J. Computational drug repositioning based on multi-similarities bilinear matrix factorization. Briefings in Bioinformatics. PMID 33147616 DOI: 10.1093/bib/bbaa267 |
0.191 |
|
| 2021 |
Zou Y, Zhu Y, Li Y, Wu FX, Wang J. Parallel computing for genome sequence processing. Briefings in Bioinformatics. PMID 33822883 DOI: 10.1093/bib/bbab070 |
0.186 |
|
| 2003 |
Li Y, Mascagni M. Improving performance via computational replication on a large-scale computational grid Proceedings - Ccgrid 2003: 3rd Ieee/Acm International Symposium On Cluster Computing and the Grid. 442-449. DOI: 10.1109/CCGRID.2003.1199399 |
0.185 |
|
| 2014 |
Ji H, Sosonkina M, Li Y. An implementation of block conjugate gradient algorithm on CPU-GPU processors Proceedings of Co-Hpc 2014: 1st International Workshop On Hardware-Software Co-Design For High Performance Computing - Held in Conjunction With Sc 2014: the International Conference For High Performance Computing, Networking, Storage and Analysis. 72-77. DOI: 10.1109/Co-HPC.2014.10 |
0.179 |
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| 2022 |
Cheng Z, Zhao Q, Li Y, Wang J. IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism. Bioinformatics (Oxford, England). PMID 35801934 DOI: 10.1093/bioinformatics/btac485 |
0.175 |
|
| 2021 |
Wu G, Yang M, Li Y, Wang J. De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten -Norm Minimization. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. PMID 33481664 DOI: 10.1089/cmb.2020.0538 |
0.17 |
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| 2023 |
Zhao H, Zhang X, Zhao Q, Li Y, Wang J. MSDRP: a deep learning model based on multi-source data for predicting drug response. Bioinformatics (Oxford, England). PMID 37606993 DOI: 10.1093/bioinformatics/btad514 |
0.168 |
|
| 2009 |
Li Y, Mascagni M, Gorin A. A decentralized parallel implementation for parallel tempering algorithm Parallel Computing. 35: 269-283. DOI: 10.1016/j.parco.2008.12.009 |
0.162 |
|
| 2022 |
Wu Y, Zeng M, Yu Y, Li Y, Li M. A Pseudo Label-wise Attention Network for Automatic ICD Coding. Ieee Journal of Biomedical and Health Informatics. PMID 35867367 DOI: 10.1109/JBHI.2022.3193291 |
0.161 |
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| 2022 |
Liu K, Li H, Li Y, Wang J, Wang J. A comparison of topologically associating domain callers based on Hi-C data. Ieee/Acm Transactions On Computational Biology and Bioinformatics. PMID 35104223 DOI: 10.1109/TCBB.2022.3147805 |
0.159 |
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| 2021 |
Zhao H, Li Y, Wang J. A Convolutional Neural Network and Graph Convolutional Network Based Method for Predicting the Classification of Anatomical Therapeutic Chemicals. Bioinformatics (Oxford, England). PMID 33769479 DOI: 10.1093/bioinformatics/btab204 |
0.155 |
|
| 2005 |
Li Y, Mascagni M. Grid-based Quasi-Monte Carlo Applications Monte Carlo Methods and Applications. 11. DOI: 10.1515/1569396054027265 |
0.141 |
|
| 2023 |
Wang B, Liu K, Li Y, Wang J. DFHiC: A dilated full convolution model to enhance the resolution of Hi-C data. Bioinformatics (Oxford, England). PMID 37084258 DOI: 10.1093/bioinformatics/btad211 |
0.119 |
|
| 2023 |
Xu Y, Li HD, Lin CX, Zheng R, Li Y, Xu J, Wang J. CellBRF: a feature selection method for single-cell clustering using cell balance and random forest. Bioinformatics (Oxford, England). 39: i368-i376. PMID 37387178 DOI: 10.1093/bioinformatics/btad216 |
0.116 |
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| 2023 |
Zhao H, Ni P, Zhao Q, Liang X, Ai D, Erhardt S, Wang J, Li Y, Wang J. Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework. Communications Biology. 6: 870. PMID 37620651 DOI: 10.1038/s42003-023-05243-w |
0.115 |
|
| 2003 |
Li Y, Mascagni M. Analysis of large-scale grid-based monte carlo applications International Journal of High Performance Computing Applications. 17: 369-382. DOI: 10.1177/10943420030174003 |
0.115 |
|
| 2011 |
Tran L, Banerjee D, Sun X, Wang J, Kumar AJ, Vinning D, McKenzie FD, Li Y, Li J. A large-scale manifold learning approach for brain tumor progression prediction Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7009: 265-272. DOI: 10.1007/978-3-642-24319-6_33 |
0.111 |
|
| 2012 |
Li Y. MOMCMC: An efficient Monte Carlo method for multi-objective sampling over real parameter space Computers & Mathematics With Applications. 64: 3542-3556. DOI: 10.1016/j.camwa.2012.09.003 |
0.106 |
|
| 2013 |
Tran L, Banerjee D, Wang J, Kumar AJ, McKenzie F, Li Y, Li J. High-dimensional MRI data analysis using a large-scale manifold learning approach Machine Vision and Applications. 24: 995-1014. DOI: 10.1007/s00138-013-0499-8 |
0.08 |
|
| 2019 |
Yu Y, Li M, Liu L, Li Y, Wang J. Clinical big data and deep learning: Applications, challenges, and future outlooks Big Data Mining and Analytics. 2: 288-305. DOI: 10.26599/BDMA.2019.9020007 |
0.078 |
|
| 2012 |
Ji H, Li Y. Reusing Random Walks in Monte Carlo Methods for Linear Systems Procedia Computer Science. 9: 383-392. DOI: 10.1016/j.procs.2012.04.041 |
0.078 |
|
| 2007 |
Li Y, Chen D, Yuan X. Trustworthy remote compiling services for grid-based scientific applications The Journal of Supercomputing. 41: 119-131. DOI: 10.1007/s11227-006-0029-9 |
0.073 |
|
| 2015 |
He W, Shen J, Tian X, Li Y, Akula V, Yan G, Tao R. Gaining competitive intelligence from social media data Evidence from two largest retail chains in the world Industrial Management and Data Systems. 115: 1622-1636. DOI: 10.1108/Imds-03-2015-0098 |
0.053 |
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| Hide low-probability matches. |