Gene Regulatory Network Inference

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Gene regulatory network inference seeks to identify which genes regulate others, and how those relationships change across cells, conditions, and time. Methods range from correlation and mutual-information networks to causal discovery, graph learning, sequence-based models, and Boolean or probabilistic models of regulatory dynamics; some combine expression with chromatin accessibility or biological priors. Perturbation data and single-cell benchmarks help test predictions, while foundation models and transfer-learning approaches aim to infer networks for unseen genes or datasets. A central challenge is separating causal regulation from co-expression when data are sparse, confounded, or context-dependent, and benchmark rankings can shift with evaluation choices.

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24 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks
Oct 5, 2026Azlaan Mustafa Samad, Wei Zhang, Adèle H RibeiroClustering Causal Discovery
L3S Research Center, Leibniz University Hannover, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany · Institute for Causal and Explainable Artificial Intelligence in Life Sciences, Center for Computational Life Sciences, RWTH Aachen University, Germany
Orchestra: Corroboration-Based Regulatory Candidate Discovery via Composed Bioinformatics MCP Agents
Aug 27, 2026Jose A. BirdCancer Genomics Multi-Agent Orchestration
Independent Researcher, Bird AI Solutions
A Learning Algorithm for Threshold Boolean Networks with Prescribed Fixed Points
Aug 18, 2026Gonzalo A. RuzBoolean Function Learning Gene Regulatory Network Inference
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile · Millennium Nucleus for Social Data Science (SODAS), Santiago, Chile · Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning), Santiago, Chile +1
Human-Guided Causal Knowledge Injection for Virtual Cells
Aug 9, 2026Pengcheng Wang, Changjian Chen, Zhuo Tang +4Causal Discovery Gene Regulatory Network Inference
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
Jul 26, 2026Yuche Gao, José Miguel Hernández-Lobato, Siyuan GuoSynthetic Data Pretraining Structural Causal Models
University of Cambridge, Cambridge, United Kingdom · Prior Labs, Freiburg, Germany
Continuous surrogates versus threshold Boolean networks for modeling Arabidopsis ISR gene regulation
Jul 25, 2026Gonzalo A. RuzSurrogate Modeling Gene Regulatory Network Inference
Facultad de Ingenier´ıa y Ciencias, Universidad Adolfo Ib´a˜nez · Millennium Nucleus for Social Data Science (SODAS) · Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning) +1
Amortized Bayesian Causal Discovery of Extended Factor Graphs
Jul 24, 2026Yichen Gu, Yuxuan Song, Weizhou Qian +2Causal Discovery Probabilistic Graphical Models
Department of Electrical and Computer Engineering University of Michigan Ann Arbor, MI 48109, USA · Department of Computational Medicine and Bioinformatics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA · Department of Statistics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA +1
BRIDGE: Bottleneck-Aware Regulator-Set Inference and Diagnosis for Cooperative Gene Regulatory Recovery
Jul 21, 2026Maryam Rahimimovassagh, Clayton Thomas Barham, Ivan Garibay +1Feature Interaction Modeling Gene Regulatory Network Inference
University of Central Florida
Deep and Probabilistic Models for Gene Regulatory Network Inference
Jul 17, 2026Claudia Skok GibbsGene Regulatory Network Inference Genomics
Center for Data Science New York University
CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion
Jul 14, 2026Jiaze Song, Runhao Zhao, Minghao Xu +2Link Prediction Gene Regulatory Network Inference
Peking University · National University of Defense Technology
Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
Jul 5, 2026Stephen Asiedu, David WatsonCausal Discovery Gene Regulatory Network Inference
Department of Informatics, King’s College London, Bush House, 30 Aldwych, WC2B 4BG, London, United Kingdom
Recovering Candidate Circadian Regulators of Arrhythmic Pituitary Hormone Genes Using Reliability-Weighted Magnetic Laplacian with rwMagLap
Jul 3, 2026Shabnam Sodagari, Nick JaspersonGraph Signal Processing Graph-Based Retrieval
BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks
Jun 2, 2026Ziyang Dong, Shanwen Tan, Hengchuang Yin +5Heterogeneous GNNs Graph SSL
College of Computer Science, Sichuan University, Chengdu, 610065, China · Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China · School of Mathematics, Sichuan University, Chengdu, 610065, China +2
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
May 30, 2026Tianyang Xu, Tianci Liu, Niraj Rayamajhi +4Transformer Gene Regulatory Network Inference
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA · Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, USA · School of Biological Sciences, Illinois State University, Normal, IL, USA
On the Recoverability of Causal Relations from Bulk Gene Expression Data
May 30, 2026Gongxu Luo, Boyang Sun, Kun ZhangStructural Causal Models Causal Discovery
Mohamed bin Zayed University of Artificial Intelligence · Carnegie Mellon University
Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT
May 8, 2026Ondřej Huvar, Nikola Beneš, Martin Jonáš +2Gene Regulatory Network Inference Constraint Satisfaction
Masaryk University, Brno, Czechia
When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data
May 6, 2026Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika +2Causal Discovery Gene Regulatory Network Inference
Faculty of Engineering University of Deusto Bilbao, Spain
Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
May 1, 2026Jiaxin Qi, Hang Li, Yan Cui +2Gene Regulatory Network Inference Single-Cell Transcriptomics
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · University of Chinese Academy of Sciences, Beijing, China
Learning biophysical models of gene regulation with probability flow matching
Apr 27, 2026Suryanarayana Maddu, Victor Chardès, Michael J. ShelleyFlow Matching Single-Cell Perturbation Modeling
Center for Computational Biology, Flatiron Institute, New York, NY, USA, 10010 · Molecular & Cellular Biology Department, Harvard University, Cambridge, MA 02138 · Courant Institute of Mathematical Sciences, New York University, New York, NY, USA, 10012
Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why?
Mar 27, 2026Bailey Andrew, David R. Westhead, Luisa CutilloGraph Structure Learning Gene Regulatory Network Inference
School of Computer Science, University of Leeds, Woodhouse, Leeds, UK; LS2 9JT · School of Molecular and Cellular Biology, University of Leeds · School of Mathematics, University of Leeds
GIP-RAG: An Evidence-Grounded Retrieval-Augmented Framework for Interpretable Gene Interaction and Pathway Impact Analysis
Mar 19, 2026Fujian Jia, Jiwen Gu, Cheng Lu +5LLM Reasoning with Graphs Gene Regulatory Network Inference
Kanghua Juntai Biotech Co. Ltd., Room 1504, Building 7, Dongwang Jingyuan, No. 768 Jingwang Road, Kunshan, Suzhou, Jiangsu Province, China · Department of Radiation Oncology, Cancer Treatment Center, The Second Affiliated Hospital of Hainan Medical University, Haikou, China · Department of Nutrition, University of California, Davis, USA +1
Quantifying Ranking Instability Across Evaluation Protocol Axes in Gene Regulatory Network Benchmarking
Mar 3, 2026Ihor KendiukhovBenchmark Design Benchmark Validity
Department of Computer Science, University of Tübingen, Germany
Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal
Feb 19, 2026Ihor KendiukhovMechanistic Interpretability Neural Network Interpretability
Department of Computer Science, University of Tübingen, Tübingen, Germany
Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms
Feb 9, 2026Nobuyuki OtaTransformer Interpretability Single-Cell Perturbation Modeling
Independent Researcher, Burlingame, CA, USA

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GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks

Azlaan Mustafa Samad, Wei Zhang, Adèle H Ribeiro

L3S Research Center, Leibniz University Hannover, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany · Institute for Causal and Explainable Artificial Intelligence in Life Sciences, Center for Computational Life Sciences, RWTH Aachen University, Germany

Clustering Causal Discovery Causal Structure Learning Gene Regulatory Network Inference

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Discovery of causal relationships in high-dimensional Gene Regulatory Networks (GRN) is computationally challenging and often difficult to interpret due to dense connections. Therefore, grouping genes together into functional modules can improve tractability and biological interpretability. However, existing cluster level causal discovery methods assume access to a predefined admissible partitions, requiring the graph over clusters to be acyclic. Constructing such partitions is therefore challenging. In this work, we introduce GO-based Clustering for Causal Discovery (GO4CD), an algorithm that uses Gene Ontology (GO) to construct biologically meaningful gene partitions at multiple levels of granularity, while favoring those more likely to be admissible for causal discovery. GO4CD groups together genes participating in a shared biological process, and propagates gene annotations through the ontology hierarchy to achieve different granularity of partitions. Furthermore, we integrate GO4CD with Causal Learning over Clusters (CLOC) algorithm and evaluate recovery of true Markov equivalence class both with an oracle of conditional independencies and on simulated gene expression data using multivariate conditional independence tests. We evaluate GO4CD on multiple E.coli regulatory subnetworks and find that it is inadmissible in 18.1% of the cases, compared with 65.3-82.3% for the semantic-similarity baselines. Our results indicate that GO4CD is substantially better suited to learning causal GRNs defined over biologically meaningful gene clusters.