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Prediction of Genes Associated With Autism Spectrum Disorder Using Sequence And Graph Embedding Methods

2025-05-23Documento de conferência Acesso restrito
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datacite.subject.fosCiências Médicas
dc.contributor.authorInácio, João
dc.contributor.authorVilela, Joana
dc.contributor.authorMarques, Ana Rita
dc.contributor.authorSantos, João Xavier
dc.contributor.authorRasga, Célia
dc.contributor.authorVicente, Astrid
dc.contributor.authorMartiniano, Hugo
dc.date.accessioned2026-03-04T14:37:38Z
dc.date.available2026-03-04T14:37:38Z
dc.date.issued2025-05-23
dc.description.abstractIntroduction: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, characterized by a strong genetic component, heterogeneous phenotypic presentation and complex genetic architecture. [1, 2] Identification of ASD risk genes and biological pathways is key to understand this disorder.[3, 4] Despite the large number of genes associated with ASD, its genetic etiology is still not fully understood. To address this challenge, we developed a machine-learning approach for ASD risk gene prediction. Methods: We developed a Machine Learning–based approach to identify and rank ASD-associated genes. Results and discussion: Prediction of ASD-associated Genes The best model was used to create a ranked list of ASD-associated genes. Using the top decile of the predicted genes, we performed a decile enrichment analysis using Phenotypes from the Human Phenotype Ontology (HPO) database and gene lists for neurodevelopmental disorders and psychiatric disorders unrelated to ASD. Conclusions: We developed a machine learning approach based on sequence and Graph embedding Methods to predict ASD-associated genes. Our approach compares favorably with state-of-the-art approaches in identifying genes targeted by loss-of-function (LOF) mutations in the MSSNG and Simons Simplex Collection cohorts and shows specificity towards ASD-related phenotypes.eng
dc.description.sponsorshipThe authors would like to acknowledge support by the UIDB/04046/2020 and UIDP/04046/2020 Centre grants from Fundação para a Ciência e a Tecnologia (FCT), Portugal to BioISI. This work was supported by FCT, through funding to the CeNAlma project (PTDC/MED-OUT/29327/2017), the DeepRe project (EXPL/CCI-BIO/0126/2021, http://doi.org/10.54499/EXPL/CCI-BIO/0126/2021 ) and the MedRefSyst project (POCI-01-0145-FEDER-016428-PAC, SAICTPAC/0010/2015). HM acknowledges support by FCT (https://doi.org/10.33499/CEECINST/00019/2018/CP1516/CT0001.
dc.identifier.urihttp://hdl.handle.net/10400.18/11126
dc.language.isoeng
dc.peerreviewedn/a
dc.relationBiosystems and Integrative Sciences Institute
dc.relationBiosystems and Integrative Sciences Institute
dc.rights.uriN/A
dc.subjectAutism Spectrum Disorder
dc.subjectPrediction of Genes
dc.subjectAutismo
dc.subjectPerturbações do Desenvolvimento Infantil e Saúde Mental
dc.titlePrediction of Genes Associated With Autism Spectrum Disorder Using Sequence And Graph Embedding Methodseng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/04046/2020
oaire.awardNumberUIDP/04046/2020
oaire.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardTitleBiosystems and Integrative Sciences Institute
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04046%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04046%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.conferencePlaceMilan, Italy
oaire.citation.titleEuropean Human Genetics Conference (ESHG), 24-27 May 2025
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcce
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isProjectOfPublicationdc433369-36fd-4935-bd52-c56aa49c72e1
relation.isProjectOfPublicatione8390b4d-1833-4925-a0ab-5fff0527efaa
relation.isProjectOfPublication.latestForDiscoverydc433369-36fd-4935-bd52-c56aa49c72e1

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