Your browser doesn't support javascript.
loading
A deep learning workflow for quantification of micronuclei in DNA damage studies in cultured cancer cell lines: A proof of principle investigation.
Panchbhai, Anand; Savash Ishanzadeh, Munuse C; Sidali, Ahmed; Solaiman, Nadeen; Pankanti, Smarana; Kanagaraj, Radhakrishnan; Murphy, John J; Surendranath, Kalpana.
Afiliação
  • Panchbhai A; Logy.AI, Machine Learning Research Division, Indian Institute of Technology Bhilai, Raipur India. Electronic address: anandp@logy.ai.
  • Savash Ishanzadeh MC; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom.
  • Sidali A; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom.
  • Solaiman N; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom.
  • Pankanti S; Logy.AI, Machine Learning Research Division, Indian Institute of Technology Bhilai, Raipur India.
  • Kanagaraj R; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom; School of Life Sciences, University of Bedfordshire, Park Square, Luton LU1 3JU, United Kingdom.
  • Murphy JJ; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom.
  • Surendranath K; Genome engineering laboratory, University of Westminster, London W1W 6UW, United Kingdom. Electronic address: k.surendranath1@westminster.ac.uk.
Comput Methods Programs Biomed ; 232: 107447, 2023 Apr.
Article em En | MEDLINE | ID: mdl-36889248
Assuntos
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article