Taming Asynchrony for Attractor Detection in Large Boolean Networks (Technical Report)
April 20, 2017 Β· Declared Dead Β· π IEEE/ACM Transactions on Computational Biology & Bioinformatics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
arXiv ID
1704.06530
Category
q-bio.MN
Cross-listed
cs.DC,
q-bio.QM
Citations
37
Venue
IEEE/ACM Transactions on Computational Biology & Bioinformatics
Last Checked
3 months ago
Abstract
Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.MN
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Large-scale analysis of disease pathways in the human interactome
R.I.P.
π»
Ghosted
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
R.I.P.
π»
Ghosted
AptRank: An Adaptive PageRank Model for Protein Function Prediction on Bi-relational Graphs
R.I.P.
π»
Ghosted
Learning of signaling networks: molecular mechanisms
R.I.P.
π»
Ghosted
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted