In one paragraphArticle in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
17 authors.
Aditya Shrawat *Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, USA.
Chuanpeng DongDepartment of Genetics, and Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA.
Yuntao YangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
S Gail EckhardtDan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
Gloria M SheynkmanDepartment of Molecular Physiology and Biological Physics, Department of Biochemistry and Molecular Genetics, and UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA, USA.ORCID http://orcid.org/0000-0002-4223-9947 Funding
Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI DAVID R ROWLEY · 2007 to 2026
$73.9MLinking genome variation to transcriptional network dynamics in human B cellsU01HG012041 · NHGRI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jishnu Das, HARINDER SINGH · 2021 to 2026
$6.1MEngagement and outreach to achieve a FAIR data ecosystem for the BICANU24MH130988 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HUA XU, Guo-Qiang ZHANG · 2022 to 2026
$4.6MStructure and Function of Immune Gene Regulatory NetworksR35GM128625 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Juan Ignacio Fuxman Bass · 2018 to 2026
$4.0MNetwork-based Framework to Decode Novel Gain-of-Function Mutations and their Mechanistic Roles in General Human DiseasesR35GM133658 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI S. Stephen Yi · 2019 to 2026
$2.6MDeciphering Functional Consequences of Specific and Combinatorial Mutations in Protein Interaction NetworksR35GM137836 · NIGMS · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI SAHNI, NIDHI · 2020 to 2023
$1.8MMechanisms of Action of Natural Genetic VariationR01HG012366 · NHGRI · STANFORD UNIVERSITY · PI Daniel Jarosz · 2023 to 2026
$1.5MIMAT-ITCR Collaboration: A Cytoscape Toolkit to Model Proteoform-Resolved Cancer NetworksR33CA281919 · NCI · UNIVERSITY OF VIRGINIA · PI Gloria Sheynkman · 2024 to 2026
$1.3MDetermining the Immunological Consequences of DNA Replication Stress Response Defects in Renal Cells Carcinoma to Improve Immunotherapy OutcomesR00CA240689 · NCI · CLEVELAND CLINIC LERNER COM-CWRU · PI MCGRAIL, DANIEL JAMES · 2022 to 2024
$747kCancer Research Institute (CRI) Lloyd Old STAR Award (CRI 4964)NIGMS NIH HHS R35 GM128625U.S. Department of Defense (United States Department of Defense) W81XWH-22-1-0164U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) P30CA125123U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) R00CA240689U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) R33CA281919U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG012366U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) U01HG012041U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM128625U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM133658U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM137836U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) U24MH130988
6 · The paper itselfAbstract
Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions-defined as 'protein codes'-remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts.
Indexed as
Computational BiologyMutationProteinsHumansProteins
Identifiers
PMID42056223
PMCPMC13259923
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC-ND
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