Refereed journal articles

  1. Yu, Z., Su, Y., Lu, Y., Yang, Y., Wang, F., Zhang, S., Chang, Y., Wong, K. C., & Li, X. (2023). Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA. Nature Communications.
  2. Zhu, H., Yang, Y., Wang, Y., Wang, F., Huang, Y., Chang, Y., Wong, K.-chun, & Li, X. (2023). Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet. Nature Communications, 14(1), 6824.
  3. Su, Y., Yu, Z., Yang, Y., Wong, K.-C., & Li, X. Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation. Advanced Science, 2307280.
  4. Fan, Y., Wang, Y., Wang, F., Huang, L., Yang, Y., Wong, K.-C., & Li, X. (2023). Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning. Advanced Science, 10(22), 2205442.
  5. Zheng, Z., Chen, J., Chen, X., Huang, L., Xie, W., Lin, Q., Li, X., & Wong, K.-C. (2023). Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD. Advanced Science, 2204113.
  6. Wang, F., Alinejad-Rokny, H., Lin, J., Gao, T., Chen, X., Zheng, Z., Meng, L., Li, X., & Wong, K.-C. (2023). A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level. Advanced Science, 2303502.
  7. Chen, N., Yu, J., Liu, Z., Meng, L., Li, X., & Wong, K.-C. (2024). Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation. Nucleic Acids Research, gkae210. https://doi.org/10.1093/nar/gkae210
  8. Wang, Y., Yu, Z., Li, S., Bian, C., Liang, Y., Wong, K.-C., & Li, X. (2023). scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering. Bioinformatics, 39(2), btad075.
  9. Sun, P., Fan, S., Li, S., Zhao, Y., Lu, C., Wong, K.-C., & Li, X. (2023). Automated exploitation of deep learning for cancer patient stratification across multiple types. Bioinformatics, 39(11), btad654.
  10. Su, Y., Wang, F., Zhang, S., Liang, Y., Wong, K.-C., & Li, X. (2022). scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information. Bioinformatics, 38(19), 4537–4545.
  11. Lu, Y., Yu, Z., Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2022). GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering. Bioinformatics, 38(11), 3020–3028. https://doi.org/10.1093/bioinformatics/btac290
  12. Wang, Y., Yang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network. Bioinformatics, 38(3), 678–686. https://doi.org/10.1093/bioinformatics/btab739
  13. Li, X., Zhang, S., & Wong, K.-C. (2018). Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning. Bioinformatics, 35(16), 2809–2817. https://doi.org/10.1093/bioinformatics/bty1056
  14. Wang, Y., Bian, C., Wong, K.-C., Li, X., & Yang, S. (2022). Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(8), 5016–5027. http://dx.doi.org/10.1109/tsmc.2021.3112049
  15. Su, Y., Zhu, H., Wong, K.-C., Chang, Y., & Li, X. (2023). Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery. IEEE Transactions on Cybernetics, 53(5), 2753–2766. http://dx.doi.org/10.1109/tcyb.2022.3208095
  16. Wang, Y., Li, X., Wong, K.-C., Chang, Y., & Yang, S. (2022). Evolutionary Multiobjective Clustering Algorithms With Ensemble for Patient Stratification. IEEE Transactions on Cybernetics, 52(10), 11027–11040. http://dx.doi.org/10.1109/tcyb.2021.3069434
  17. Li, X., Zhang, S., & Wong, K.-C. (2021). Multiobjective Genome-Wide RNA-Binding Event Identification From CLIP-Seq Data. IEEE Transactions on Cybernetics, 51(12), 5811–5824. http://dx.doi.org/10.1109/tcyb.2019.2960515
  18. Li, X., & Wong, K.-C. (2019). Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification. IEEE Transactions on Cybernetics, 49(5), 1680–1693. http://dx.doi.org/10.1109/tcyb.2018.2817480
  19. Wang, Y., Hou, Z., Yang, Y., Wong, K.-chun, & Li, X. (2022). Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework. PLOS Computational Biology, 18(12), e1010779. http://dx.doi.org/10.1371/journal.pcbi.1010779
  20. Cheng, Y., Su, Y., Yu, Z., Liang, Y., Wong, K.-C., & Li, X. (2023). Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5036–5044. http://dx.doi.org/10.1609/aaai.v37i4.25631
  21. Yu, Z., Lu, Y., Wang, Y., Tang, F., Wong, K.-C., & Li, X. (2022). ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4671–4679. http://dx.doi.org/10.1609/aaai.v36i4.20392
  22. Hou, Z., Yang, Y., Ma, Z., Wong, K.-C., & Li, X. (2022). EDLMPPI: Learning the Protein Language of Proteome-wide Protein-protein Binding Sites via Explainable Ensemble Deep Learning. Communications Biology. http://dx.doi.org/10.21203/rs.3.rs-1775089/v1
  23. Wang, F., Gao, T., Lin, J., Zheng, Z., Huang, L., Toseef, M., Li, X., & Wong, K.-C. (2022). GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data. IScience, 25(12), 105535. http://dx.doi.org/10.1016/j.isci.2022.105535
  24. Wang, Y., Wong, K.-C., & Li, X. (2022). Exploring high-throughput biomolecular data with multiobjective robust continuous clustering. Information Sciences, 583, 239–265. http://dx.doi.org/10.1016/j.ins.2021.11.030
  25. Toseef, M., Olayemi Petinrin, O., Wang, F., Rahaman, S., Liu, Z., Li, X., & Wong, K.-C. (2023). Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. Briefings in Bioinformatics, 24(4). http://dx.doi.org/10.1093/bib/bbad254
  26. Huang, L., Lin, J., Liu, R., Zheng, Z., Meng, L., Chen, X., Li, X., & Wong, K.-C. (2022). CoaDTI: multi-modal co-attention based framework for drug–target interaction annotation. Briefings in Bioinformatics, 23(6). http://dx.doi.org/10.1093/bib/bbac446
  27. Toseef, M., Li, X., & Wong, K.-C. (2022). Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning. Briefings in Bioinformatics, 23(3). http://dx.doi.org/10.1093/bib/bbac078
  28. Yang, Y., Hou, Z., Wang, Y., Ma, H., Sun, P., Ma, Z., Wong, K.-C., & Li, X. (2022). HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network. Briefings in Bioinformatics, 23(2). http://dx.doi.org/10.1093/bib/bbac027
  29. Huang, L., Lin, J., Li, X., Song, L., Zheng, Z., & Wong, K.-C. (2021). EGFI: drug–drug interaction extraction and generation with fusion of enriched entity and sentence information. Briefings in Bioinformatics, 23(1). http://dx.doi.org/10.1093/bib/bbab451
  30. Li, X., Li, S., Huang, L., Zhang, S., & Wong, K.-chun. (2021). High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning. Briefings in Bioinformatics, 23(1). http://dx.doi.org/10.1093/bib/bbab368
  31. Hou, Z., Yang, Y., Li, H., Wong, K.-chun, & Li, X. (2021). iDeepSubMito: identification of protein submitochondrial localization with deep learning. Briefings in Bioinformatics, 22(6). http://dx.doi.org/10.1093/bib/bbab288
  32. Yu, Z., Bian, C., Liu, G., Zhang, S., Wong, K.-C., & Li, X. (2021). Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbab125
  33. Li, X., Zhang, S., & Wong, K.-C. (2021). Deep embedded clustering with multiple objectives on scRNA-seq data. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbab090
  34. Yang, Y., Li, S., Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). Identification of haploinsufficient genes from epigenomic data using deep forest. Briefings in Bioinformatics, 22(5). http://dx.doi.org/10.1093/bib/bbaa393
  35. Li, X., Li, S., Wang, Y., Zhang, S., & Wong, K.-C. (2020). Identification of pan-cancer Ras pathway activation with deep learning. Briefings in Bioinformatics, 22(4). http://dx.doi.org/10.1093/bib/bbaa258
  36. Yang, Y., Hou, Z., Ma, Z., Li, X., & Wong, K.-C. (2020). iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network. Briefings in Bioinformatics, 22(4). http://dx.doi.org/10.1093/bib/bbaa274
  37. Li, X., & Wong, K.-C. (2018). Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization. IEEE Journal of Biomedical and Health Informatics, 22(5), 1619–1629. http://dx.doi.org/10.1109/jbhi.2017.2769711
  38. Li, X., & Li, M. (2015). Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem. IEEE Transactions on Engineering Management, 62(4), 544–557. http://dx.doi.org/10.1109/tem.2015.2453264
  39. Li, X., & Ma, S. (2017). Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times. IEEE Transactions on Engineering Management, 64(2), 149–165. http://dx.doi.org/10.1109/tem.2016.2645790
  40. Li, X., Zhang, S., & Wong, K.-C. (2021). Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2445–2458. http://dx.doi.org/10.1109/tcbb.2020.2971993
  41. Wang, Y., Ma, Z., Wong, K.-C., & Li, X. (2021). Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2431–2444. http://dx.doi.org/10.1109/tcbb.2020.2974953
  42. Li, X., & Wong, K.-C. (2019). Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. http://dx.doi.org/10.1109/tcbb.2019.2906601
  43. Li, X., Zhang, S., & Wong, K.-C. (2018). Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. http://dx.doi.org/10.1109/tcbb.2018.2849759
  44. Li, X., & Wong, K.-C. (2019). Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(1), 272–282. http://dx.doi.org/10.1109/tcbb.2017.2776224
  45. Li, X., & Wong, K.-C. (2018). A Comparative Study for Identifying the Chromosome-Wide Spatial Clusters from High-Throughput Chromatin Conformation Capture Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3), 774–787. http://dx.doi.org/10.1109/tcbb.2017.2684800
  46. Wong, K.-C., Yan, S., Lin, Q., Li, X., & Peng, C. (2020). Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1), 327–333. http://dx.doi.org/10.1109/tcbb.2018.2882548
  47. Li, X., & Yin, M. (2013). Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data. IEEE Transactions on NanoBioscience, 12(4), 343–353. http://dx.doi.org/10.1109/tnb.2013.2294716
  48. Li, X., Ma, S., & Wong, K.-C. (2017). Evolving Spatial Clusters of Genomic Regions From High-Throughput Chromatin Conformation Capture Data. IEEE Transactions on NanoBioscience, 16(6), 400–407. http://dx.doi.org/10.1109/tnb.2017.2725991
  49. Wang, Y., Liu, B., Ma, Z., Wong, K.-C., & Li, X. (2019). Nature-Inspired Multiobjective Cancer Subtype Diagnosis. IEEE Journal of Translational Engineering in Health and Medicine, 7, 1–12. http://dx.doi.org/10.1109/jtehm.2019.2891746

Book

  1. (2019). In Natural Computing for Unsupervised Learning. Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-98566-4

Preprints