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DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.
Mahdi-Esferizi, Roohallah; Haji Molla Hoseyni, Behnaz; Mehrpanah, Amir; Golzade, Yazdan; Najafi, Ali; Elahian, Fatemeh; Zadeh Shirazi, Amin; Gomez, Guillermo A; Tahmasebian, Shahram.
Afiliación
  • Mahdi-Esferizi R; Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran.
  • Haji Molla Hoseyni B; Laboratory of Systems Biology and Bioinformatics (LBB), University of Tehran, Tehran, Iran.
  • Mehrpanah A; Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran.
  • Golzade Y; Department of Mathematics, Faculty of Basic Sciences, Iran University of Science and Technology,(IUST), Tehran, Iran.
  • Najafi A; Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Elahian F; Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran.
  • Zadeh Shirazi A; Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia.
  • Gomez GA; Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia.
  • Tahmasebian S; Cellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran. stahmasebian@gmail.com.
BMC Bioinformatics ; 24(1): 275, 2023 Jul 04.
Article en En | MEDLINE | ID: mdl-37403016
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido