Cargando…
Deep Learning Methods for Predicting Disease Status Using Genomic Data
Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major...
Autores principales: | Wu, Qianfan, Boueiz, Adel, Bozkurt, Alican, Masoomi, Arya, Wang, Allan, DeMeo, Dawn L, Weiss, Scott T, Qiu, Weiliang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530791/ https://www.ncbi.nlm.nih.gov/pubmed/31131151 |
Ejemplares similares
-
Improved prediction of smoking status via isoform-aware RNA-seq deep learning models
por: Wang, Zifeng, et al.
Publicado: (2021) -
New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs
por: Yu, Danyang, et al.
Publicado: (2019) -
A Comparative Study of Tests for Homogeneity of Variances with Application to DNA Methylation Data
por: Li, Xuan, et al.
Publicado: (2015) -
Detecting Differentially Variable MicroRNAs via Model-Based Clustering
por: Li, Xuan, et al.
Publicado: (2018) -
Differential DNA methylation marks and gene comethylation of COPD in African-Americans with COPD exacerbations
por: Busch, Robert, et al.
Publicado: (2016)