Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients DOI Open Access
Jorge J. Cebolla, Pilar Giraldo, Jessica Gómez

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(16), С. 8586 - 8586

Опубликована: Авг. 6, 2024

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications GD1. Although imaging studies are gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, needed for rapid management skeletal complications. This study aimed to identify potential protein biomarkers capable predicting diagnosis bone in GD1 patients using artificial intelligence. An silico was performed novel Therapeutic Performance Mapping System methodology construct mathematical models GD1-associated at level. Pathophysiological characterization before modeling, data science strategy applied predicted activity each classifiers. Statistical criteria were used prioritize promising candidates, 18 candidates identified. Among them, PDGFB, IL1R2, PTH CCL3 (MIP-1α) highlighted due their ease measurement blood. proposes validated tool discover new support clinician decision-making an area where medical needs have not yet been met. However, confirming results vitro and/or vivo necessary.

Язык: Английский

Current status of mannose receptor-targeted drug delivery for improved anti-HIV therapy DOI
Satish Rojekar, Amol D. Gholap, Namdev Togre

и другие.

Journal of Controlled Release, Год журнала: 2024, Номер 372, С. 494 - 521

Опубликована: Июнь 27, 2024

Язык: Английский

Процитировано

14
Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients DOI Open Access
Jorge J. Cebolla, Pilar Giraldo, Jessica Gómez

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(16), С. 8586 - 8586

Опубликована: Авг. 6, 2024

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications GD1. Although imaging studies are gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, needed for rapid management skeletal complications. This study aimed to identify potential protein biomarkers capable predicting diagnosis bone in GD1 patients using artificial intelligence. An silico was performed novel Therapeutic Performance Mapping System methodology construct mathematical models GD1-associated at level. Pathophysiological characterization before modeling, data science strategy applied predicted activity each classifiers. Statistical criteria were used prioritize promising candidates, 18 candidates identified. Among them, PDGFB, IL1R2, PTH CCL3 (MIP-1α) highlighted due their ease measurement blood. proposes validated tool discover new support clinician decision-making an area where medical needs have not yet been met. However, confirming results vitro and/or vivo necessary.

Язык: Английский

Процитировано

0