ArticleBMC medical informatics and decision making2017
ApreciseKUre: an approach of Precision Medicine in a Rare Disease.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 27 citations in OpenAlex.
- Homogentisate 1,2-dioxygenase (HGD) gene variants, their analysis and genotype-phenotype correlations in the largest cohort of patients with AKU.European journal of human genetics : EJHG · 2019Trial
- Plasma Proteomic Signatures in Alkaptonuria.Biology · 2026Article
- Quantifying the Functional Gap in Alkaptonuria Through Machine Learning and Clinical Data Integration.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Alkaptonuria.Nature reviews. Disease primers · 2024Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.International journal of molecular sciences · 2022Article
- Article
- A robust bacterial high-throughput screening system to evaluate single nucleotide polymorphisms of human homogentisate 1,2-dioxygenase in the context of alkaptonuria.Scientific reports · 2022Article
- Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease.Frontiers in molecular medicine · 2022Review
- HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase.Current research in structural biology · 2022Article
- Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.International journal of molecular sciences · 2021Article
- Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.Orphanet journal of rare diseases · 2020Article
- Alkaptonuria: Current Perspectives.The application of clinical genetics · 2020Review
- Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlkaptonuria (AKU; OMIM:203500) is a classic Mendelian genetic disorder described by Garrod already in 1902. It causes urine to turn black upon exposure to air and also leads to ochronosis as well as early osteoarthritis. Our objective is the implementation of a Precision Medicine (PM) approach to AKU. We present here a novel ApreciseKUre database facilitating the collection, integration and analysis of patient data in order to create an AKU-dedicated "PM Ecosystem" in which genetic, biochemical and clinical resources can be shared among registered researchers. In order to exploit the ApreciseKUre database, we developed an analytic method based on Pearson's correlation coefficient and P value that generates as refreshable correlation matrix. A complete statistical analysis is obtained by associating every pair of parameters to examine the dependence between multiple variables at the same time. SHORT
conclusionsEmploying this analytic approach, we showed that some clinically used biomarkers are not suitable as prognostic biomarkers in AKU for a more reliable patients' clinical monitoring. We believe this database could be a good starting point for the creation of a new clinical management tool in AKU, which will lead to the development of a deeper knowledge network on the disease and will advance its treatment. Moreover, our approach can serve as a personalization model paradigm for other inborn errors of metabolism or rare diseases in general.
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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.