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Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.
Smith, Brandi Patrice; Auvil, Loretta Sue; Welge, Michael; Bushell, Colleen Bannon; Bhargava, Rohit; Elango, Navin; Johnson, Kamin; Madak-Erdogan, Zeynep.
Afiliación
  • Smith BP; Department of Food Science and Human Nutrition, University of Illinois, 1201 W Gregory Dr, Urbana-ChampaignUrbana, IL, 61801, USA.
  • Auvil LS; Illinois Informatics Institute, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Welge M; National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Bushell CB; National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Bhargava R; Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Elango N; National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Johnson K; Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
  • Madak-Erdogan Z; Carle Illinois College of Medicine, University of Illinois, Urbana-Champaign, Urbana, IL, USA.
Sci Rep ; 10(1): 19128, 2020 11 05.
Article en En | MEDLINE | ID: mdl-33154507
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Marcadores Genéticos / Agroquímicos / Enfermedad Hepática Inducida por Sustancias y Drogas / Aprendizaje Automático Supervisado / Hígado Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Marcadores Genéticos / Agroquímicos / Enfermedad Hepática Inducida por Sustancias y Drogas / Aprendizaje Automático Supervisado / Hígado Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido