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Computational frameworks integrating deep learning and statistical models in mining multimodal omics data.
Lac, Leann; Leung, Carson K; Hu, Pingzhao.
Afiliação
  • Lac L; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Leung CK; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada.
  • Hu P; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Biochemistry, Western University, London, Ontario, Canada; Department of Computer Science, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada; The Children's Health Research Institute, Lawson Health Research Institute, London, Ontario, Canada. Elect
J Biomed Inform ; 152: 104629, 2024 04.
Article em En | MEDLINE | ID: mdl-38552994
ABSTRACT

BACKGROUND:

In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms. METHODS AND

RESULTS:

The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica / Aprendizado Profundo Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Genômica / Aprendizado Profundo Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá