Towards perturbation prediction of biological networks using deep learning
Abstract The mapping of the physical interactions between biochemical entities enables quantitative analysis of dynamic biological living systems. While developing a precise dynamical model on biological entity interaction is still challenging due to the limitation of kinetic parameter detection of...
Saved in:
Main Authors: | Diya Li, Jianxi Gao |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2019
|
Subjects: | |
Online Access: | https://doaj.org/article/2276c93b000442c8a1ca26ef5fa7ff00 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Earthquakes and very deep groundwater perturbation mutually induced
by: Dugin Kaown, et al.
Published: (2021) -
Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction
by: Li-Hsin Cheng, et al.
Published: (2021) -
Predicting the clinical management of skin lesions using deep learning
by: Kumar Abhishek, et al.
Published: (2021) -
Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity
by: Syed Ashiqur Rahman, et al.
Published: (2019) -
Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
by: Bettina Knapp, et al.
Published: (2013)