Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Gene expression integration and similarity score-based modeling improve risk stratification in idiopathic venous thrombophilia.

Journal of thrombosis and haemostasis : JTH - 1 Aug 2026

Ezquerra-Condeminas Pol, Martinez-Perez Angel, Howald Cedric, Brown Andrew A, Souto Juan Carlos, Viñuela Ana, Buil Alfonso, Perera-Lluna Alexandre, Soria Jose Manuel

Abstract excerpt

BACKGROUND: Idiopathic venous thromboembolism (VTE) occurs in the absence of provoking factors, limiting the efficacy of current risk stratification. In parallel, the lack of integration between transcriptomic data and established risk factors prevents the identification of individuals with a high baseline predisposition. OBJECTIVES: We aimed to improve risk stratification of idiopathic VTE beyond traditional...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Machine Learning
Share to Predictive Learning Models

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Predictive Value of Tests

Share to Sequence Analysis, RNA

Share to Thrombophilia

Share to Transcriptome

Share to Venous Thromboembolism

Share to von Willebrand Factor

Source

Identifiers

PMID: 42167585
DOI: 10.1016/j.jtha.2026.05.015

Genes

ABO
FAM13A
FAM177B
FLVCR2
GPRIN1
PRKRA
STS
UQCRC2

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Gene expression integration and similarity score-based modeling improve risk stratification in idiopathic venous thrombophilia.PMID 42167585Read ↗

Identification of Venous Thromboembolism Driver Genes Based on Integrative Bioinformatics and Machine LearningSemantic proximity Read ↗

Plasma proteomics enhances genetic risk prediction and biological understanding of venous thromboembolismSemantic proximity Read ↗

Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism.Semantic proximity Read ↗

Determining Potential Therapeutic Targets for Venous Thrombosis through Network Pharmacology, Cluster Typing, WGCNA, Machine Learning, Immune Infiltration Analysis, and ceRNA NetworksSemantic proximity Read ↗

Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism.Semantic proximity Read ↗

Identification of potential biomarkers of venous thromboembolism in patients with COVID-19 via an integrated bioinformatic-based studySemantic proximity Read ↗

Molecular determinants of thrombosis recurrence risk across venous thromboembolism subtypesSemantic proximity Read ↗

Polygenic risk scores and risk stratification in deep vein thrombosis.Semantic proximity Read ↗

Integrated Transcriptome-Wide Profiling And Protein Structure Analysis of Pathogenic Genes In Venous ThromboembolismSemantic proximity Read ↗

Proteome-wide mendelian randomization identifies causal plasma proteins in venous thromboembolism development.Semantic proximity Read ↗

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors.Semantic proximity Read ↗

Shared genetic etiology and causality between COVID-19 and venous thromboembolism: evidence from genome-wide cross trait analysis and bi-directional Mendelian randomization studySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.