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RESEARCH PRODUCT

Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure

Ana Patiño-garcíaBegoña Martinez-montoroPatricia Diaz-gimenoIsmael Henarejos-castilloFrancisco Javier Gracia-aznárezJosé RemohíPedro RoyoGorka Alkorta-aranburuAlejandro AlemánPatricia Sebastian-leonMonica Romeu

subject

0301 basic medicineInfertilityOncologygenomic taxonomymedicine.medical_specialtyprecision medicineovarian failurePopulationMedicine (miscellaneous)BiologyGenoma humàArticlewhole exome sequencing03 medical and health sciences0302 clinical medicineInternal medicinemedicinesingle nucleotide variantFertility preservationeducationGeneExome sequencingeducation.field_of_study030219 obstetrics & reproductive medicinebusiness.industryRpersonalized medicinePrecision medicinemedicine.diseaseprediction modelMinor allele frequency030104 developmental biologyGinecologiaMedicineovaryPersonalized medicineinfertilitybusinessgenome variant profile

description

Ovarian failure (OF) is a common cause of infertility usually diagnosed as idiopathic, with genetic causes accounting for 10–25% of cases. Whole-exome sequencing (WES) may enable identifying contributing genes and variant profiles to stratify the population into subtypes of OF. This study sought to identify a blood-based gene variant profile using accumulation of rare variants to promote precision medicine in fertility preservation programs. A case–control (n = 118, n = 32, respectively) WES study was performed in which only non-synonymous rare variants &lt

yearjournalcountryeditionlanguage
2021-06-01Journal of Personalized Medicine
https://doi.org/10.3390/jpm11070609