A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data
Abstract Background Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all p...
Main Authors: | Tianyu Kang, Wei Ding, Luoyan Zhang, Daniel Ziemek, Kourosh Zarringhalam |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2017-12-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s12859-017-1984-2 |
Similar Items
-
Feedforward Neural Networks with a Hidden Layer Regularization Method
by: Habtamu Zegeye Alemu, et al.
Published: (2018-10-01) -
Reverse Engineering of Biological Systems
Published: (2014) -
Modelling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response
by: Maria E. Manioudaki, et al.
Published: (2013-06-01) -
From dynamics to links: a sparse reconstruction of the topology of a neural network
by: Aletti Giacomo, et al.
Published: (2019-01-01) -
Fuzzy Neural Networks based on Fuzzy Logic Neurons Regularized by Resampling Techniques and Regularization Theory for Regression Problems
by: Paulo Vitor de Campos Souza, et al.
Published: (2018-11-01)