arXiv AI July 16, 2026 By Jiaze Song, Runhao Zhao, Minghao Xu, Bin Cui, Wentao Zhang

CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion

arXiv:2607. 13120v1 Announce Type: cross Abstract: Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 16

BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

arXiv:2606. 14734v1 Announce Type: cross Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs.

By Ziyang Dong, Shanwen Tan, Hengchuang Yin, Wei Liu, Yifan Wang, Siyu Yi, Jiancheng Lv, Wei Ju

benchmarks

arXiv Machine Learning

Jul 20

Deep and Probabilistic Models for Gene Regulatory Network Inference

arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.

By Claudia Skok Gibbs

llms fine-tuning

arXiv Machine Learning

Jun 2

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.

By Tianyang Xu, Tianci Liu, Niraj Rayamajhi, Ryan Patrick, Kranthi Varala, Ying Li, Jing Gao

llms multimodal

arXiv AI

Jul 3

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.

By Wenbo Zhang

rag multimodal benchmarks

arXiv Machine Learning

Jun 16

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.

By Ziye Zhou, Meijie Wang, Lun Yu

arXiv Machine Learning

Jun 25

Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations

arXiv:2606. 24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult.

By Sajib Acharjee Dip, Liqing Zhang

benchmarks

Related stories

BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

Deep and Probabilistic Models for Gene Regulatory Network Inference

Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations