Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data

Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data

Abstract Background Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful fo...

Full description

Bibliographic Details
Main Authors: Savvas Kinalis, Finn Cilius Nielsen, Ole Winther, Frederik Otzen Bagger
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Bioinformatics
Subjects:
Interpretable machine learning
Deep learning
Neural networks
Manifold learning
Expression profiles
Single-cell RNA-sequencing
Online Access:http://link.springer.com/article/10.1186/s12859-019-2952-9

Internet

http://link.springer.com/article/10.1186/s12859-019-2952-9

Similar Items