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CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs).
Aspromonte, Maria Cristina; Conte, Alessio Del; Zhu, Shaowen; Tan, Wuwei; Shen, Yang; Zhang, Yexian; Li, Qi; Wang, Maggie Haitian; Babbi, Giulia; Bovo, Samuele; Martelli, Pier Luigi; Casadio, Rita; Althagafi, Azza; Toonsi, Sumyyah; Kulmanov, Maxat; Hoehndorf, Robert; Katsonis, Panagiotis; Williams, Amanda; Lichtarge, Olivier; Xian, Su; Surento, Wesley; Pejaver, Vikas; Mooney, Sean D; Sunderam, Uma; Srinivasan, Rajgopal; Murgia, Alessandra; Piovesan, Damiano; Tosatto, Silvio C E; Leonardi, Emanuela.
Afiliação
  • Aspromonte MC; Department of Biomedical Sciences, University of Padova.
  • Conte AD; Department of Biomedical Sciences, University of Padova.
  • Zhu S; Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
  • Tan W; Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
  • Shen Y; Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843.
  • Zhang Y; CUHK Shenzhen Research Institute, Shenzhen.
  • Li Q; CUHK Shenzhen Research Institute, Shenzhen.
  • Wang MH; CUHK Shenzhen Research Institute, Shenzhen.
  • Babbi G; Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna.
  • Bovo S; Department of Agricultural and Food Sciences, University of Bologna.
  • Martelli PL; Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna.
  • Casadio R; Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna.
  • Althagafi A; Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23.
  • Toonsi S; Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23.
  • Kulmanov M; Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23.
  • Hoehndorf R; Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23.
  • Katsonis P; Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030.
  • Williams A; Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030.
  • Lichtarge O; Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030.
  • Xian S; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195.
  • Surento W; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195.
  • Pejaver V; Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
  • Mooney SD; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195.
  • Sunderam U; Innovation Labs, Tata Consultancy Services, Hyderabad.
  • Srinivasan R; Innovation Labs, Tata Consultancy Services, Hyderabad.
  • Murgia A; Department of Women's and Children's Health, University of Padova.
  • Piovesan D; Department of Biomedical Sciences, University of Padova.
  • Tosatto SCE; Department of Biomedical Sciences, University of Padova.
  • Leonardi E; Department of Biomedical Sciences, University of Padova.
Res Sq ; 2023 Aug 02.
Article em En | MEDLINE | ID: mdl-37577579
ABSTRACT
In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.
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Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Res Sq Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Res Sq Ano de publicação: 2023 Tipo de documento: Article