Approximate distance correlation for selecting highly interrelated genes across datasets
With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Mor...
Enregistré dans:
Auteurs principaux: | Qunlun Shen, Shihua Zhang |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Public Library of Science (PLoS)
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/4e466ea68f9f4bc4aed5a9e8d1d109c5 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Approximate distance correlation for selecting highly interrelated genes across datasets.
par: Qunlun Shen, et autres
Publié: (2021) -
Approximate Bayesian computation.
par: Mikael Sunnåker, et autres
Publié: (2013) -
Prioritizing and characterizing functionally relevant genes across human tissues.
par: Gowthami Somepalli, et autres
Publié: (2021) -
Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens
par: Rong Xiang, et autres
Publié: (2021) -
A mixture of delta-rules approximation to bayesian inference in change-point problems.
par: Robert C Wilson, et autres
Publié: (2013)