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About

I am currently a PhD student at Biomedicine Discovery Institute of Monash University, majoring in Machine learning and Bioinformatics. My research topics include biological problems of Cancer image analysis and DNA/RNA/Protein sequence analysis, which are more challenges to application in terms of high-dimensional with small size datasets, unpredictable heterogeneous data, lack of annotations, etc. I am keen to apply machine learning algorithms to the data of reality to solve challenging analysis tasks. Stay playing, stay breathing!

Ongoing Projects

  • [Research] Advanced applications of deep learning on H&E WSI: a systematic review.

This review focuses on the studies utilizing deep learning methods to predict intrinsic information of cancers that beyonds the human’s capacity to tell directly from the H&E whole slide images. By searching and screening the records from databases of Pubmed, IEEE, Web of Sci, and EMBASE, 42 queries were obtained for systematic review. This review follows the PRISMA guidelines and focuses on the research category, adopted model and model performance, advantages, and shortcomings.

  • [Research] Prognostic biomarker discovery using graph neural networks by integrating multi-omics data of gastric cancer.
  • [Research] Gene essentiality prediction.

Completed projects

  • [Research] DEMoS: a deep learning-based ensemble method for molecular subtyping of gastric cancer from histopathology images
  • [Research] OCTID: a one-class learning-based Python package for tumor image detection
  • [iOS APP Dev] Pandee Pinyin: Fro Chinese Pinyin learning
  • [Research] HEAL: an automated deep learning framework for cancer histopathology image analysis
  • [iOS APP Dev] An APP for realtime COVID-19 data sharing

Publications

  • Wang, Y., Wang, Y., Hu, C., Li, M., Fan, Y., Otter, N., Sam, I., Gou, H., Hu, Y., & Kwok, T. (2021). Cell graph neural networks enable digital staging of tumour microenvironment and precisely predict patient survival in gastric cancer. medRxiv.
  • Chen, Z., Zhao, P., Li, F., Wang, Y., Smith, A. I., Webb, G. I., Akutsu, T., Baggag, A., Bensmail, H., & Song, J. (2020). Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences. Briefings in Bioinformatics, 21(5), 1676-1696.
  • Li, M., Wang, Y., Li, F., Zhao, Y., Liu, M., Zhang, S., Bin, Y., Smith, A. I., Webb, G., & Li, J. (2020). A deep learning-based method for identification of bacteriophage-host interaction. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • Wang, Y., Li, F., Bharathwaj, M., Rosas, N. C., Leier, A., Akutsu, T., Webb, G. I., Marquez-Lago, T. T., Li, J., & Lithgow, T. (2021). DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases. Briefings in Bioinformatics, 22(4), bbaa301.
  • Wang, Y., Coudray, N., Zhao, Y., Li, F., Hu, C., Zhang, Y.-Z., Imoto, S., Tsirigos, A., Webb, G. I., & Daly, R. J. (2021). HEAL: an automated deep learning framework for cancer histopathology image analysis. Bioinformatics.
  • Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Wang, Y., Webb, G. I., Smith, A. I., Daly, R. J., & Chou, K.-C. (2018). iFeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 34(14), 2499-2502.
  • Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N. D., Webb, G. I., & Chou, K.-C. (2019). iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Briefings in Bioinformatics, 20(2), 638-658.
  • Wang, Y., Song, J., Marquez-Lago, T. T., Leier, A., Li, C., Lithgow, T., Webb, G. I., & Shen, H.-B. (2017). Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites. Scientific Reports, 7(1), 1-15.
  • Chen, Y.-Z., Wang, Z.-Z., Wang, Y., Ying, G., Chen, Z., & Song, J. (2021). nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning. Briefings in Bioinformatics.
  • Wang, Y., Yang, L., Webb, G. I., Ge, Z., & Song, J. (2021). OCTID: a one-class learning-based Python package for tumor image detection. Bioinformatics.
  • Li, F., Leier, A., Liu, Q., Wang, Y., Xiang, D., Akutsu, T., Webb, G. I., Smith, A. I., Marquez-Lago, T., & Li, J. (2020). Procleave: predicting protease-specific substrate cleavage sites by combining sequence and structural information. Genomics, proteomics & bioinformatics, 18(1), 52-64.
  • Li, F., Wang, Y., Li, C., Marquez-Lago, T. T., Leier, A., Rawlings, N. D., Haffari, G., Revote, J., Akutsu, T., & Chou, K.-C. (2019). Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. Briefings in Bioinformatics, 20(6), 2150-2166.
  • Wang, Q., Verma, J., Vidan, N., Wang, Y., Tucey, T. M., Lo, T. L., Harrison, P. F., See, M., Swaminathan, A., & Kuchler, K. (2020). The YEATS domain histone crotonylation readers control virulence-related biology of a major human pathogen. Cell Reports, 31(3), 107528.
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