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DeepMethy: A deep learning model for protein methylation site prediction

Published in BIBM 2024 DLB2H Workshop, 2024

In this paper, a deep neural network based model is proposed to predict protein methylation, which is called by DeepMethy.

Recommended citation: Songye Gao^†, Cheng Xin^†, Sisi Ou, Jinru Li, Yanchun Liang and Xiaohu Shi^*(2024). "Paper Title Number 1." DeepMethy: A deep learning model for protein methylation site prediction. BIBM 2024 DLB2H Workshop.
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Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Published in bioRxiv, 2024

Clair3-RNA is a deep learning-based variant caller designed for small variant detection in long-read RNA-seq data. It improves both accuracy and speed by combining signal-level features, splicing-aware alignment, and attention-based neural network architectures. This tool enables high-throughput variant discovery from transcriptomic data in research and clinical applications.

Recommended citation: Zhenxian Zheng, Xian Yu, Lei Chen, Yan-Lam Lee, Cheng Xin, et al.(2024). Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data. bioRxiv.
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LVstyler: LoRa-enhanced Varied High-Quality Texture Generation with Text Alignment

Published in NeurIPS 2025 Conference Submission, 2025

LVstyler, an innovative generative framework that extends beyond the conventional 0-to-1 UV texture synthesis, specializing in a more varied style transfer for 3D meshes while preserving geometric fidelity.

Recommended citation: Hokit Fung, chengyangdong, Cheng Xin, Jie Li, Adela S.M. Lau, Guosheng Yin^*(2025).LVstyler: LoRa-enhanced Varied High-Quality Texture Generation with Text Alignment. NeurIPS 2025 Conference Submission

Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy

Published in Journal 1, 2025

This paper systematically reviews recent advances in artificial intelligence (AI), with a particular focus on machine learning (ML), across the entire drug discovery pipeline.

Recommended citation: Junwei Su^†, Cheng Xin^†, Ao Shang, Shan Wu, Zhenzhen Xie, Ruogu Xiong, Xiaoyu Xu, Cheng Zhang, Guang Chen, Yau-Tuen Chan, Guoyi Tang^*, Ning Wang, Yong Xu, Yibin Feng^*(2025). Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy. Submit to Acta Pharmaceutica Sinica B.

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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