Cargando…
Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer
Background: In recent years, the development and diagnosis of secondary cancer have become the primary concern of cancer survivors. A number of studies have been developing strategies to extract knowledge from the clinical data, aiming to identify important risk factors that can be used to prevent t...
Autores principales: | Ye, Xiucai, Li, Hongmin, Sakurai, Tetsuya, Shueng, Pei-Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643128/ https://www.ncbi.nlm.nih.gov/pubmed/31341408 http://dx.doi.org/10.7150/ijms.33820 |
Ejemplares similares
-
Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites
por: Jiao, Shihu, et al.
Publicado: (2023) -
Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity
por: Huang, Meng, et al.
Publicado: (2022) -
m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation
por: Ao, Chunyan, et al.
Publicado: (2023) -
Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning
por: Ye, Xiucai, et al.
Publicado: (2020) -
Ensemble learning for the early prediction of neonatal jaundice with genetic features
por: Deng, Haowen, et al.
Publicado: (2021)