ArticleScientific reports2021
An artificial neural network approach integrating plasma proteomics and genetic data identifies PLXNA4 as a new susceptibility locus for pulmonary embolism.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 19 citations in OpenAlex.
- Proteomic Biomarkers in VTE: From Discovery to Clinical Translation.Journal of clinical medicine · 2026Review
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Automatic ovarian follicle detection using object detection models.Scientific reports · 2024Article
- Genetic factors, risk prediction and AI application of thrombotic diseases.Experimental hematology & oncology · 2024Review
- Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis.Bleeding, thrombosis and vascular biology · 2024Article
- Population structure and selective signature of Kirghiz sheep by Illumina Ovine SNP50 BeadChip.PeerJ · 2024Article
- Host genetic polymorphisms involved in long-term symptoms of COVID-19.Emerging microbes & infections · 2023Article
- Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.PLoS genetics · 2022Article
- Proteomics in thrombosis research.Research and practice in thrombosis and haemostasis · 2022Article
- The alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension, a study with transcriptome sequencing and bioinformatic analysis.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
18 authors at 5 institutions in 3 countries.
Funding
Abstract
Venous thromboembolism is the third common cardiovascular disease and is composed of two entities, deep vein thrombosis (DVT) and its potential fatal form, pulmonary embolism (PE). While PE is observed in ~ 40% of patients with documented DVT, there is limited biomarkers that can help identifying patients at high PE risk. To fill this need, we implemented a two hidden-layers artificial neural networks (ANN) on 376 antibodies and 19 biological traits measured in the plasma of 1388 DVT patients, with or without PE, of the MARTHA study. We used the LIME algorithm to obtain a linear approximate of the resulting ANN prediction model. As MARTHA patients were typed for genotyping DNA arrays, a genome wide association study (GWAS) was conducted on the LIME estimate. Detected single nucleotide polymorphisms (SNPs) were tested for association with PE risk in MARTHA. Main findings were replicated in the EOVT study composed of 143 PE patients and 196 DVT only patients. The derived ANN model for PE achieved an accuracy of 0.89 and 0.79 in our training and testing sets, respectively. A GWAS on the LIME approximate identified a strong statistical association peak (rs1424597: p = 5.3 × 10
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