ArticleNPJ systems biology and applications2021
Prediction of hemophilia A severity using a small-input machine-learning framework.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The 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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- The use of artificial intelligence in the prevention and management of bleeding disorders: a systematic review.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.International journal of molecular sciences · 2025Review
- Article
- Full-scale network analysis reveals properties of the FV protein structure organization.Scientific reports · 2023Article
- A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A.Frontiers in bioinformatics · 2023Article
- Article
- Computational analyses reveal fundamental properties of the AT structure related to thrombosis.Bioinformatics advances · 2023Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- A Machine Learning Framework Predicts the Clinical Severity of Hemophilia B Caused by Point-Mutations.Frontiers in bioinformatics · 2022Article
- Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure - including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.