Literature DB >> 30316064

A new integrated and interactive tool applicable to inborn errors of metabolism: Application to alkaptonuria.

Ottavia Spiga1, Vittoria Cicaloni2, Andrea Zatkova3, Lia Millucci4, Giulia Bernardini5, Andrea Bernini6, Barbara Marzocchi7, Monica Bianchini8, Andrea Zugarini9, Alberto Rossi10, Matteo Zazzeri11, Alfonso Trezza12, Bruno Frediani13, Lakshminarayan Ranganath14, Daniela Braconi15, Annalisa Santucci16.   

Abstract

This paper describes our experience with the development and implementation of a database for the rare disease Alkaptonuria (AKU, OMIM: 203500). AKU is an autosomal recessive disorder caused by a gene mutation leading to the accumulation of homogentisic acid (HGA). Analogously to other rare conditions, currently there are no approved biomarkers to monitor AKU progression or severity. Although some biomarkers are under evaluation, an extensive biomarker analysis has not been undertaken in AKU yet. In order to fill this gap, we gained access to AKU-related data that we carefully processed, documented and stored in a database, which we named ApreciseKUre. We undertook a suitable statistical analysis by associating every couple of potential biomarkers to highlight significant correlations. Our database is continuously updated allowing us to find novel unpredicted correlations between AKU biomarkers and to confirm system reliability. ApreciseKUre includes data on potential biomarkers, patients' quality of life and clinical outcomes facilitating their integration and possibly allowing a Precision Medicine approach in AKU. This framework may represent an online tool that can be turned into a best practice model for other rare diseases.
Copyright © 2018. Published by Elsevier Ltd.

Entities:  

Keywords:  Alkaptonuria; Biomarkers; Data analysis; Database; Precision medicine; Rare disease

Mesh:

Substances:

Year:  2018        PMID: 30316064     DOI: 10.1016/j.compbiomed.2018.10.002

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  4 in total

1.  Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

Authors:  Vittoria Cicaloni; Ottavia Spiga; Giovanna Maria Dimitri; Rebecca Maiocchi; Lia Millucci; Daniela Giustarini; Giulia Bernardini; Andrea Bernini; Barbara Marzocchi; Daniela Braconi; Annalisa Santucci
Journal:  FASEB J       Date:  2019-08-28       Impact factor: 5.834

2.  Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.

Authors:  Ottavia Spiga; Vittoria Cicaloni; Cosimo Fiorini; Alfonso Trezza; Anna Visibelli; Lia Millucci; Giulia Bernardini; Andrea Bernini; Barbara Marzocchi; Daniela Braconi; Filippo Prischi; Annalisa Santucci
Journal:  Orphanet J Rare Dis       Date:  2020-02-12       Impact factor: 4.123

3.  Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.

Authors:  Ottavia Spiga; Vittoria Cicaloni; Anna Visibelli; Alessandro Davoli; Maria Ausilia Paparo; Maurizio Orlandini; Barbara Vecchi; Annalisa Santucci
Journal:  Int J Mol Sci       Date:  2021-01-26       Impact factor: 5.923

4.  HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase.

Authors:  Malancha Karmakar; Vittoria Cicaloni; Carlos H M Rodrigues; Ottavia Spiga; Annalisa Santucci; David B Ascher
Journal:  Curr Res Struct Biol       Date:  2022-08-30
  4 in total

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