Xiaoyong Pan

University of Copenhagen

Denmark

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Articles (20)

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2025

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2022

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2021

Collaborators (8)

Poul Nissen

DENMARK

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Jacob Giehm Mikkelsen

Aarhus University

DENMARK

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Rasmus Kock Flygaard

Assistant Professor

Aarhus University

DENMARK

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Mark Denham

Associate Professor

Aarhus University

DENMARK

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Line Reinert

Aarhus University

DENMARK

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Manutea Christophe Serrero

Aarhus University

DENMARK

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Trine Hyrup Mogensen

Aarhus University

DENMARK

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Emil Aagaard Thomsen

Aarhus University

DENMARK

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Xiaoyong Pan

Research Interests

Contact this professor

Articles (20)

ASCE-PPIS: a protein–protein interaction sites predictor based on equivariant graph neural network with fusion of structure-aware pooling and graph collapse

Base-resolution binding profile prediction of proteins on RNAs with deep learning

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes

m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics

DRAG: design RNAs as hierarchical graphs with reinforcement learning

CATransUnetLBP: Accurate Prediction of Protein-Ligand Binding Pockets Using a Hybrid Network

ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks

TMEFF1 is a neuron-specific restriction factor for herpes simplex virus

FishAI: Automated hierarchical marine fish image classification with vision transformer

Predicting transcriptional changes induced by molecules with MiTCP

MLNGCF: circRNA–disease associations prediction with multilayer attention neural graph-based collaborative filtering

De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment

ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites

Leveraging scaffold information to predict protein–ligand binding affinity with an empirical graph neural network

MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction

Cell clustering for spatial transcriptomics data with graph neural networks

Fast protein structure comparison through effective representation learning with contrastive graph neural networks

GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier

lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning

Collaborators (8)

Poul Nissen

Jacob Giehm Mikkelsen

Rasmus Kock Flygaard

Mark Denham

Line Reinert

Manutea Christophe Serrero

Trine Hyrup Mogensen

Emil Aagaard Thomsen

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