Key papers

• Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning (2026)
  Jiayuan Ding et al.
  4.95
• Improving scDiffusion with Sparsity-Biased Classifier-Free Guidance (2026)
  Yu Song et al.
  3.45
• ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics (2026)
  Omar Coser
  3.23
• Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement (2026)
  Abdul Moeed et al.
  1.89
• A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data (2026)
  Ziwei Huang et al.
  1.83
• CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification (2026)
  Boyang Fan et al.
  1.78
• Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data (2026)
  Zhuomin Liang et al.
  1.67
• Hierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data (2026)
  Thomas Vaitses Fontanari and Mariana Recamonde-Mendoza
  1.61
• engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection (2026)
  Tiantian Yang et al.
  1.61
• SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model (2026)
  Xianghao Zhan et al.
  1.61
• Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs (2025)
  Bang Chen et al.
  1.44
• Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis (2025)
  Premt Cara et al.
  1.44
• AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining (2025)
  Jahidul Arafat et al.
  1.44
• PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction (2025)
  Ranga Baminiwatte et al.
  1.39
• A Bayesian approach to model uncertainty in single-cell genomic data (2025)
  Shanshan Ren et al.
  1.33
• MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification (2025)
  Tiantian Yang et al.
  1.33
• Graph Neural Networks as a Substitute for Transformers in Single-Cell Transcriptomics (2025)
  Jiaxin Qi et al.
  1.28
• Soft Graph Clustering for single-cell RNA Sequencing Data (2025)
  Ping Xu et al.
  1.28
• BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool (2025)
  Vicente Ramos (1) et al.
  1.28
• SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics (2025)
  Roxana Zahedi et al.
  1.22
• PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone (2025)
  Kamilia Zaripova et al.
  1.22
• LapDDPM: A Conditional Graph Diffusion Model for scRNA-seq Generation with Spectral Adversarial Perturbations (2025)
  Lorenzo Bini et al.
  1.22
• Missing-Modality-Aware Graph Neural Network for Cancer Classification (2025)
  Sina Tabakhi et al.
  1.22
• Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking (2025)
  Alireza Sadeghi et al.
  1.17
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype (2025)
  Changxi Chi et al.
  1.17
• scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data (2025)
  Ping Xu et al.
  1.17
• GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction (2025)
  Yoshitaka Inoue et al.
  1.11
• A Novel Graph Transformer Framework for Gene Regulatory Network Inference (2025)
  Binon Teji and Swarup Roy
  1.11
• Large Language Models for Zero-shot Inference of Causal Structures in Biology (2025)
  Izzy Newsham et al.
  1.06
• GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction (2025)
  Jonas Chris Ferrao et al.
  1.06
• Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data (2025)
  Yu-An Huang et al.
  1.00
• PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks (2024)
  Jose L. Mellina Andreu et al.
  0.44
• Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks (2020)
  Arijit Sehanobish et al.
  —
• Self-supervised edge features for improved Graph Neural Network training (2020)
  Arijit Sehanobish et al.
  —
• EPGAT: Gene Essentiality Prediction With Graph Attention Networks (2020)
  Jo\~ao Schapke et al.
  —
• GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis (2020)
  Kateryna Melnyk et al.
  —
• Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts (2020)
  Aparna Elangovan et al.
  —
• A step towards neural genome assembly (2020)
  Lovro Vr\v{c}ek et al.
  —
• Bacteriophage classification for assembled contigs using Graph Convolutional Network (2021)
  Jiayu Shang and Jingzhe Jiang and Yanni Sun
  —
• GraphBreak: Tool for Network Community based Regulatory Medicine, Gene co-expression, Linkage Disequilibrium analysis, functional annotation and more (2021)
  Abhishek Narain Singh
  —
• Graph Representation Learning in Biomedicine (2021)
  Michelle M. Li et al.
  —
• DNA-GCN: Graph convolutional networks for predicting DNA-protein binding (2021)
  Yuhang Guo et al.
  —
• Graph Representation Learning on Tissue-Specific Multi-Omics (2021)
  Amine Amor (1) et al.
  —
• SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network (2021)
  Ziyi Liu et al.
  —
• AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks (2021)
  Ruiwei Feng et al.
  —
• RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning (2021)
  Hansheng Xue et al.
  —
• CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model (2022)
  Jiayu Shang and Yanni Sun
  —
• Topological data analysis of truncated contagion maps (2022)
  Florian Klimm
  —
• Graph Neural Networks for Microbial Genome Recovery (2022)
  Andre Lamurias et al.
  —
• Learning to Untangle Genome Assembly with Graph Convolutional Networks (2022)
  Lovro Vr\v{c}ek et al.
  —
• Fast sequence to graph alignment using the graph wavefront algorithm (2022)
  Haowen Zhang et al.
  —
• ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis (2022)
  Can Firtina et al.
  —
• Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information (2022)
  Yulun Wu et al.
  —
• SHINE: SubHypergraph Inductive Neural nEtwork (2022)
  Yuan Luo
  —
• Granger causal inference on DAGs identifies genomic loci regulating transcription (2022)
  Rohit Singh et al.
  —
• Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction (2022)
  Hansheng Xue et al.
  —
• Graph Neural Networks for Breast Cancer Data Integration (2022)
  Teodora Reu
  —
• Graph Contrastive Learning for Multi-omics Data (2023)
  Nishant Rajadhyaksha and Aarushi Chitkara
  —
• Graph Neural Operators for Classification of Spatial Transcriptomics Data (2023)
  Junaid Ahmed and Alhassan S. Yasin
  —
• A Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification (2023)
  Bingjun Li et al.
  —