{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["49(3)"],"submitter":["Salvador R"],"pubmed_abstract":["Background and hypothesis
The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprints geometrical patterns may require flexible algorithms capable of characterizing such complexity.Study design
Based on an initial sample of scanned fingerprints from 612 patients with a diagnosis of non-affective psychosis and 844 healthy subjects, we have built deep learning classification algorithms based on convolutional neural networks. Previously, the general architecture of the network was chosen from exploratory fittings carried out with an independent fingerprint dataset from the National Institute of Standards and Techno"],"journal":["Schizophrenia bulletin"],"pagination":["738-745"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10154725"],"repository":["biostudies-literature"],"pubmed_title":["Fingerprints as Predictors of Schizophrenia: A Deep Learning Study."],"pmcid":["PMC10154725"],"pubmed_authors":["McKenna PJ","Feliz Munoz C","Sevillano De La Fuente P","Negro-Gonzalez E","Castillo-Oramas E","Sanchez Perez M","Aguilar-Valero T","Maria Santiago-Bautista J","Vicente Jauregui Berecibar J","Pomarol-Clotet E","Sarro S","Porta-Pelayo D","Rios-Alvarado Z","Corte-Souto C","Almodovar-Paya C","Guardiola-Ripoll M","Camano Serna C","Munoz Hermoso P","Del Olmo-Romero F","Antonio Larraz-Romeo J","Botillo-Martin C","Felices de la Fuente A","Salvador R","Fatjo-Vilas Mestre M","Martin-Carrasco M","Bosque Gabarre C","Garcia-Leon MA","Felipe Martinez E","Herrero-Munecas P","Martin-Lorenzo C","Gil-Sanz D","Gonzalez-Pablos E","Arrece Iriondo I","Maria Castells Bescos E","Rebolleda Gil C","Feria-Raposo I","HHFingerprints Group","Dominguez Panchon A","Larrubia-Romero J"],"additional_accession":[]},"is_claimable":false,"name":"Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.","description":"Background and hypothesis
The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprints geometrical patterns may require flexible algorithms capable of characterizing such complexity.Study design
Based on an initial sample of scanned fingerprints from 612 patients with a diagnosis of non-affective psychosis and 844 healthy subjects, we have built deep learning classification algorithms based on convolutional neural networks. Previously, the general architecture of the network was chosen from exploratory fittings carried out with an independent fingerprint dataset from the National Institute of Standards and Techno","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-04T11:14:24.473Z","creation":"2025-04-04T11:14:24.473Z"},"accession":"S-EPMC10154725","cross_references":{"pubmed":["36444899"],"doi":["10.1093/schbul/sbac173"]}}