ArticleNature methods2016
Inferring causal molecular networks: empirical assessment through a community-based effort.
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Article in Nature methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
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108citing papers in PubMed
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Who cites it
108 citing papers in PubMed.
- A framework to map cAMP signalling nanodomains: phosphodiesterase-centred phosphoproteome-interactome networks (pPINs) in cardiac myocytes.Journal of molecular endocrinology · 2026Review
- Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions.Nature communications · 2026Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- Computational Approaches for Pathway-Centric Analysis of Protein Post-Translational Modifications.Proteomics · 2025Review
- Coupling causality and interpretable machine learning to reveal the reaction coordinate of C-N coupling with a supramolecular Cu-calix[8]arene catalyst.Digital discovery · 2025Article
- Quantifying interventional causality by knockoff operation.Science advances · 2025Article
- Vistla: identifying influence paths with information theory.Bioinformatics (Oxford, England) · 2025Article
- Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables.Journal of machine learning research : JMLR · 2025Article
- Informeasure: an R/bioconductor package for quantifying nonlinear dependence between variables in biological networks from an information theory perspective.BMC bioinformatics · 2024Article
- NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states.Computational and structural biotechnology journal · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Estimation of multiple networks with common structures in heterogeneous subgroups.Journal of multivariate analysis · 2024Article
- Optimal linear ensemble of binary classifiers.Bioinformatics advances · 2024Article
- Article
- EnsInfer: a simple ensemble approach to network inference outperforms any single method.BMC bioinformatics · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Review
48 more citing papers are in PubMed but not listed here.
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Authors and funding
39 authors.
Steven M HillMRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
Laura M HeiserDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
Thomas CokelaerEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
Michael UngerAutomatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
Nicole K NesserDepartment of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
Daniel E CarlinDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Yang ZhangDepartment of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
Artem SokolovDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Evan O PaullDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Chris K WongDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Kiley GraimDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.ORCID http://orcid.org/0000-0002-4569-8444
Adrian BivolDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Haizhou WangDepartment of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
Fan ZhuDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Bahman AfsariDepartment of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
Ludmila V DanilovaDepartment of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.ORCID http://orcid.org/0000-0003-2813-3094
Alexander V FavorovDepartment of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
Wai Shing LeeDepartment of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
Dane TaylorStatistical and Applied Mathematical Sciences Institute, Research Triangle Park, North Carolina, USA.ORCID http://orcid.org/0000-0003-1851-3309
Chenyue W HuDepartment of Bioengineering, Rice University, Houston, Texas, USA.
Byron L LongDepartment of Bioengineering, Rice University, Houston, Texas, USA.
David P NorenDepartment of Bioengineering, Rice University, Houston, Texas, USA.
Alexander J BisbergDepartment of Bioengineering, Rice University, Houston, Texas, USA.
HPN-DREAM Consortium
Gordon B MillsDepartment of Systems Biology, MD Anderson Cancer Center, Houston, Texas, USA.
Joe W GrayDepartment of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
Michael KellenSage Bionetworks, Seattle, Washington, USA.ORCID http://orcid.org/0000-0002-4096-7078
Thea NormanSage Bionetworks, Seattle, Washington, USA.
Stephen FriendSage Bionetworks, Seattle, Washington, USA.ORCID http://orcid.org/0000-0002-0830-7600
Amina A QutubDepartment of Bioengineering, Rice University, Houston, Texas, USA.
Elana J FertigDepartment of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
Yuanfang GuanDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Mingzhou SongDepartment of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.ORCID http://orcid.org/0000-0002-6883-6547
Joshua M StuartDepartment of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Paul T SpellmanDepartment of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
Heinz KoepplAutomatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
Gustavo StolovitzkyIBM Translational Systems Biology and Nanobiotechnology, Yorktown Heights, New York, USA.
Julio Saez-RodriguezEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.ORCID http://orcid.org/0000-0002-8552-8976
Sach MukherjeeMRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
Funding
Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8MSystems-Based Predictions of Responses to Cancer TherapyU54CA112970 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI GRAY, JOE W. · 2004 to 2014
$27.1MTraining SubprojectU54HG007990 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, PATTERSON, DAVID · 2014 to 2018
$14.8MTRANS-NETWORK PROJECTSU54CA143869 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI MARKO, JOHN F · 2009 to 2013
$12.8MInterpretable deep learning models for translational medicine RenewalR01LM012011 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Tanner J. Freeman, XINGHUA LU · 2015 to 2026
$3.6MBIGDATA: Mid-Scale DCM: DA: ESCE: Discovering Molecular ProcessesR01CA180778 · NCI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI STUART, JOSHUA MICHAEL · 2013 to 2017
$3.5MNew Integrative Pathway Analysis Methods to Predict Biomedical OutcomesR01GM109031 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI STUART, JOSHUA MICHAEL · 2014 to 2018
$2.9MA New Generation Clinical Decision Support SystemR01LM011663 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JIANG, XIA, NEAPOLITAN, RICHARD E · 2014 to 2017
$2.0MDynamical Models of Cetuximab Resistance in HNSCC Based on Serial Genomics DataR01CA177669 · NCI · JOHNS HOPKINS UNIVERSITY · PI FERTIG, ELANA · 2014 to 2019
$1.8MDeveloping Graph Models and Efficient Algorithms for the Study of Cancer DiseaseR00LM011673 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LU, SONGJIAN · 2015 to 2017
$656kDetecting Genome Wide Epistasis with Efficient Bayesian Network LearningR00LM010822 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JIANG, XIA · 2012 to 2014
$565kIdentifying Malignant Cell Signaling from Protein Interactions an Polyomic DataK25CA141053 · NCI · JOHNS HOPKINS UNIVERSITY · PI FERTIG, ELANA · 2010 to 2014
$419kMedical Research Council MC_UP_1302/1NCI NIH HHS 5R01CA180778NCI NIH HHS K25 CA141053NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA177669NCI NIH HHS R01 CA180778NCI NIH HHS U54 CA112970NCI NIH HHS U54 CA143869NCI NIH HHS U54CA143869NHGRI NIH HHS U54 HG007990NIGMS NIH HHS 1R01GM109031NIGMS NIH HHS R01 GM109031NLM NIH HHS R00 LM010822NLM NIH HHS R00LM010822NLM NIH HHS R00 LM011673NLM NIH HHS R01 LM011663NLM NIH HHS R01LM011663NLM NIH HHS R01 LM012011
Abstract
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
Indexed as
CausalityGene Regulatory NetworksSoftwareSystems BiologyAlgorithmsComputational BiologyComputer SimulationGene Expression ProfilingHumansModels, BiologicalNeoplasmsProtein Interaction MappingSignal TransductionTumor Cells, Cultured
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