For more publications, you may check it out on my [ResearchGate] or [Google Scholar].

(*Corresponding  authorship)

[12] Zhang S., Li X., Lin Q., Lin J., & Wong K. C.*. (2020) Uncovering the key dimensions of high-throughput biomolecular data using deep learning. Nucleic Acids Research, 48(10), e56, 2020. [IF 2020 = 16.971] Top

[11] Lin, J., Zhang, Z., Zhang, S., Chen, J., & Wong, K. C. (2020). CRISPR-Net: a recurrent convolutional network quantifies CRISPR off-target activities with mismatches and indels. Advanced Science, 7(13), 1903562. [IF 2020 = 16.806] Top

[10] Li X., Zhang S., & Wong K. C.*. (2020) Multiobjective genome-wide RNA-binding event identification from CLIP-seq data. IEEE Transactions on Cybernetics. [IF 2020 = 11.448] Top CCF A 

[9] Li X., Zhang, S., & Wong K. C.*. (2020) Evolving transcriptomic profiles from single-cell RNA-Seq data using nature-inspired multiobjective optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics. [IF 2020 = 3.71] CCF B

[8] Zhang S., Li X., Lin Q., Lin J., & Wong K. C.*. (2019) Nature-inspired compressed sensing for transcriptomic profiling from random composite measurement. IEEE Transactions on Cybernetics. [IF 2020 = 11.079] Top CCF A

[7] Zhang S., Li X., Lin Q., Lin J., & Wong K. C.*. (2019) Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications. Bioinformatics. 35 (7), 1108-1115. [IF 2019 = 5.610] Top CCF B

[6] Li X., Zhang, S., & Wong K. C.*. (2019) Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning. Bioinformatics, 35(16), 2809-2817. [IF 2019 = 5.610] Top CCF B

[5] Wong K. C.*, Chen J., Zhang J., Lin J., Yan S., Zhang S., Li X., Liang C., Peng C., Lin Q., Kwong S., Yu J. (2019) Early cancer detection from multianalyte blood test results. iScience, 15, 332-341. [IF 2019 = 4.447]

[4] 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, 17(1), 226-237. [IF 2018 = 2.896] CCF B

[3] Zhang W., Zhang S., & Zhang S. * (2018). Two-factor high-order fuzzy-trend fts model based on BSO-FCM and improved KA for TAIEX stock forecasting. Nonlinear Dynamics, 94(2), 1429-1446. [IF 2018 = 4.604]

[2] Zhang W., Zhang S., Zhang S.*, Yu D., & Huang, N. (2018). A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting. Soft Computing, 23 (16), 6979-6994. [IF 2018 = 2.784] CCF C

[1] Zhang W.*, Zhang S., Zhang S., Yu D., & Huang, N. (2017). A multi-factor and high-order stock forecast model based on type-2 FTS using cuckoo search and self-adaptive harmony search. Neurocomputing, 240, 13-24. [IF 2017 = 3.241] CCF C