Xi'an Jiaotong-Liverpool University

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Dr. Daiyun Huang received his BSc degree in Mathematics from the University of Liverpool in 2017, his MSc degree in Statistical Science from the University of Oxford in 2018, and his Ph.D. degree in Computer Science from the University of Liverpool in July 2022. He joined the Academy of Pharmacy at XJTLU as a postdoctoral fellow in November 2022.

His previous research revolved around developing deep learning models for biological sequences, especially for RNA modification research. His current research interests mainly focus on AI-aided drug discovery, including developing deep learning methods for high-throughput virtual screening and de novo generation of hit-like molecules. Special attention is paid to out-of-distribution problems and protein representation learning.

Qualifications
- Ph.D., University of Liverpool, 2022
- MSc, University of Oxford, 2018
- BSc, University of Liverpool, 2017
Experience
- Postdoc, Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, 2022-present
Research interests
- AI-aided drug discovery: high-throughput virtual screening and de novo generation of hit-like molecules.
- Prediction and engineering of non-coding sequences of mRNA-based therapeutics based on deep learning
- Develop computational models of biological sequences (DNA, RNA, and proteins) to address meaningful biological questions, such as predicting the effect of mutations on protein function.
Articles
- Zhang, Y., Jiang, J., Ma, J., Wei, Z.*, Wang, Y., Song, B., Meng, J., Jia, G., de Magalhães, J. P., Rigden, D. J., Huang, D.*, & Chen, K.* (2023). DirectRMDB: a database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology. Nucleic acids research, 51(D1), D106–D116.
- Song, B., Wang, X., Liang, Z., Ma, J., Huang, D., Wang, Y., de Magalhães, J. P., Rigden, D. J., Meng, J., Liu, G., Chen, K., & Wei, Z. (2023). RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication. Nucleic acids research, 51(D1), D1388–D1396.
- Huang, D.*, Chen, K., Song, B., Wei, Z., Su, J., Coenen, F., de Magalhães, J. P., Rigden, D. J., & Meng, J.* (2022). Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. Nucleic acids research, 50(18), 10290–10310.
- Song, B., Huang, D.*, Zhang, Y., Wei, Z., Su, J., Pedro de Magalhães, J., Rigden, D. J., Meng, J., & Chen, K.* (2022). m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues. Genomics, proteomics & bioinformatics, S1672-0229(22)00114-0. Advance online publication.
- Zhou, J., Wang, X., Wei, Z., Meng, J., & Huang, D.* (2022). 4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences. Molecular therapy. Nucleic acids, 30, 337–345.
- Li, Z., Mao, J., Huang, D.*, Song, B., & Meng, J.* (2022). RNADSN: Transfer-Learning 5-Methyluridine (m5U) Modification on mRNAs from Common Features of tRNA. International journal of molecular sciences, 23(21), 13493.
- Pan, S., Zhang, Y., Wei, Z., Meng, J., & Huang, D.* (2022). Prediction and motif analysis of 2’-O-methylation using a hybrid deep learning model from RNA primary sequence and nanopore signals. Current Bioinformatics, 17(9), 873 - 882.
- Wang, Y., Li, J., Huang, D., Hao, Y., Li, B., Wang, K., Chen, B., Li, T., & Liu, X. (2022). Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis. Biomolecules, 12(7), 906.
- Ma, J., Song, B., Wei, Z., Huang, D., Zhang, Y., Su, J., de Magalhães, J. P., Rigden, D. J., Meng, J., & Chen, K. (2022). m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome. Nucleic acids research, 50(D1), D196–D203.
- Zhang, Y., Huang, D., Wei, Z., & Chen, K. (2022). Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data. Methods, 203, 62–69.
- Liang, Z., Zhang, L., Chen, H., Huang, D., & Song, B. (2022). m6A-Maize: Weakly supervised prediction of m6A-carrying transcripts and m6A-affecting mutations in maize (Zea mays). Methods, 203, 226–232.
- Zhao, X., Zhang, Y., Huang, D., Meng, J., & Wei, Z. (2022). Detecting RNA modification using direct RNA sequencing: A systematic review. Computational and structural biotechnology journal, 20, 5740–5749.
- Liu, H., Wang, G., Song, S., Huang, D., & Zhang, L. (2022). RC-Net: Regression Correction for End-To-End Chromosome Instance Segmentation. Frontiers in genetics, 13, 895099.
- Song, Z., Huang, D.*, Song, B., Chen, K., Song, Y., Liu, G., Su, J., Magalhães, J. P., Rigden, D. J., & Meng, J.* (2021). Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nature communications, 12(1), 4011.
- Huang, D., Song, B., Wei, J., Su, J., Coenen, F., & Meng, J.* (2021). Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Bioinformatics, 37(Suppl_1), i222–i230.
Conference presentations
- Huang, D., Song, B., Wei, J., Su, J., Coenen, F., & Meng, J. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Oral presentation in ISMB/ECCB 2021, Virtual Events. (2021).
Grants
- Jiangsu Science and Technology Programme - Young-scholar Programme (No. BK20230279), 200,000 RMB, 2023-07 to 2026-06, Principal Investigator
- National Natural Science Foundation of China - Young Scientists Fund (No. 32300552), 300,000 RMB, 2024-01 to 2026-12, Principal Investigator
Teaching activities
- APH101-2223-S2-Biostatistics and R Programming
- APH102-2223-S2-Mathematical Modeling
Awards and honours
- 2022年入选“苏州市自然科学优秀学术论文一等奖”（SZLW202206，2020-2021年度）

电话
+86 (0)51281883213
Email
Daiyun.Huang@xjtlu.edu.cn
地址
SD334A(SIP Campus-Science Building)
Suzhou Dushu Lake Science and Education Innovation District
Suzhou Industrial Park
Suzhou
P.R.China
215123

Daiyun HuangPh.D.

Profile

Qualifications

Experience

Research interests

Articles

Conference presentations

Grants

Teaching activities

Awards and honours