Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance-Reference-Cited by-同舟云学术

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Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance

Published:2020-11 Issue: Volume:110 Page:101976
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Macías-García Laura,Martínez-Ballesteros María,Luna-Romera José María,García-Heredia José M.,García-Gutiérrez Jorge,Riquelme-Santos José C.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Medicine (miscellaneous)

Reference102 articles.

1. Cancer fact sheets;International Agency for Research on Cancer – World Health Organization,2019

2. Breast cancer statistics;World Cancer Research Fund,2019

3. Identification of methylation markers and differentially expressed genes with prognostic value in breast cancer;Wu;J Comput Biol,2019

4. Classification of large DNA methylation datasets for identifying cancer drivers;Celli;Big Data Res,2018

5. Ultra high-dimensional nonlinear feature selection for big biological data;Yamada;IEEE Trans Knowl Data Eng,2018

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2. A framework for predicting breast cancer recurrence;Expert Systems with Applications;2024-04

3. Advancing Breast Cancer Drug Delivery: The Transformative Potential of Bioinformatics and Artificial Intelligence;Current Cancer Therapy Reviews;2024-03-13

4. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors;ACS Sensors;2024-02-16

5. Breast cancer recurrence prediction with deep neural network and feature optimization;Automatika;2024-01-02

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