Fuyi Li
@lifuyi1991Machine Learning, Data Mining, Computational Biology
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I am pleased to share our another deep learning method for single-cell RNA-seq clustering. See the full-text below: academic.oup.com/bib/article/25…
I am pleased to share our new research in scRNA-seq data analysis; we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. See full-text here👇 academic.oup.com/bib/article/25…
Advancing microRNA target site prediction with transformer and base-pairing patterns academic.oup.com/nar/article/52…
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects @BriefingBioinfo 1/ This review explores state-of-the-art deep learning methods for predicting the effects of non-coding genetic variants, highlighting their growing…
Check out our new survey. LLMs have broad impacts on every stage of drug discovery, from understanding disease mechanisms to virtual screening to clinical trials.
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials @geochurch 1. This review explores the transformative potential of Large Language Models (LLMs) in drug discovery and development, from understanding disease mechanisms to…
📢📷 I am recruiting 1 Postdoc research fellow and 2~3 PhD students in AI for Cancer research to join the ABI lab at @SAiGENCI Topics include but are not limited to: (1) AI for Drug discovery; (2) Multiomics; (3) Structural bioinformatics. careers.adelaide.edu.au/cw/en/job/5148…
I am pleased to share our new research in collaboration with @ShiruiPan in leveraging graph neural networks to learn RNA structure patterns and advanced mRNA subcellular localization prediction. See full-text here👇 academic.oup.com/bioinformatics…
🧬 Excited to share our latest preprint: we propose Mimosa, a new computational approach based on Transformer and Base-Pairing Patterns to enhance the prediction of miRNA targets @supercs08 @giwebb biorxiv.org/content/10.110…
🧬 Excited to share our recent study: we introduce MERITS, the first comprehensive 3D structure database specifically designed for PE/PPE proteins. @supercs08 @lachlanjmc @Blueheaven_Chen academic.oup.com/bioinformatics…
🧬Excited to share our latest preprint: we introduce scSimGCL, a framework based on graph contrastive learning for self-supervised pretraining of GNNs. This framework facilitates the generation of high-quality representations crucial for cell clustering. biorxiv.org/content/10.110…
🧬 Excited to share our latest preprint: we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction!
Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction. arxiv.org/abs/2405.02628
Nature research paper: Accurate structure prediction of biomolecular interactions with AlphaFold 3 go.nature.com/44AJ3Sz
TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion #deeplearning pubs.acs.org/doi/10.1021/ac… @lifuyi1991 @supercs08 #JCIM Vol64 Issue4 #Bioinformatics
We are pleased to share PLANNER, a multi-scale deep language model for the origins of replication site prediction, published in the IEEE Journal of Biomedical and Health Informatics. @lachlanjmc @supercs08 @ShiruiPan Check the full text here: ieeexplore.ieee.org/document/10380…
Totally agree!
"Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There's no question that digital biology is going to be it." Jensen Huang, founder & CEO of NVIDIA.
We're pleased to announce that Fuyi Li (@lifuyi1991), of @supercs08 group (@TheDohertyInst @SAiGENCI, @MonashBDI) is the winner of the poster prize on General Computational Biology at #ISMBECCB2023! RSC Chemical Biology is proud to have sponsored this award.
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