Cargando…
Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise
There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals i...
Autores principales: | Mallik, Saurav, Zhao, Zhongming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747741/ https://www.ncbi.nlm.nih.gov/pubmed/33335112 http://dx.doi.org/10.1038/s41598-020-78463-3 |
Ejemplares similares
-
Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm
por: Mallik, Saurav, et al.
Publicado: (2018) -
Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles
por: Mallik, Saurav, et al.
Publicado: (2019) -
ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis
por: Mallik, Saurav, et al.
Publicado: (2017) -
A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data
por: Mallik, Saurav, et al.
Publicado: (2020) -
Dimensionality Reduction and Louvain Agglomerative Hierarchical Clustering for Cluster-Specified Frequent Biomarker Discovery in Single-Cell Sequencing Data
por: Seth , Soumita, et al.
Publicado: (2022)