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DiseaseNet: a transfer learning approach to noncommunicable disease classification.
Gore, Steven; Meche, Bailey; Shao, Danyang; Ginnett, Benjamin; Zhou, Kelly; Azad, Rajeev K.
Afiliación
  • Gore S; Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.
  • Meche B; Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA.
  • Shao D; Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.
  • Ginnett B; Department of Engineering, Eastern Arizona College, Thatcher, AZ, USA.
  • Zhou K; Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA.
  • Azad RK; Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA. Rajeev.Azad@unt.edu.
BMC Bioinformatics ; 25(1): 107, 2024 Mar 11.
Article en En | MEDLINE | ID: mdl-38468193
ABSTRACT
As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedades no Transmisibles Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedades no Transmisibles Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos