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Reverse engineering module networks by PSO-RNN hybrid modeling
Y. Zhang
, J. Xuan
, B. G. De Los Reyes
,
R. Clarke
, H. W. Ressom
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
1
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Scopus citations
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Keyphrases
Transcriptional Network
100%
Reverse Engineering
100%
Functional Types
100%
Recurrent Neural Network
100%
Category Information
100%
Hybrid Modeling
100%
Network Modules
100%
Particle Swarm Optimization Neural Network
100%
Biological Processes
50%
Central Nervous System
50%
Network Model
50%
Expression Profile
50%
Gene Expression Data
50%
Regulatory Relationship
50%
Expression Gene
50%
Gene Ontology
50%
Underlying Network
50%
Module Selection
50%
Cell Cycle Activity
50%
Yeast Cell Cycle
50%
Regulatory Network Inference
50%
Network Inference
50%
Network Inference Methods
50%
Fuzzy C-means Clustering
50%
Neural Network Method
50%
Similar Expression
50%
Computer Science
Particle Swarm Optimization
100%
Reverse Engineering
100%
Recurrent Neural Network
100%
Network Inference
100%
Hybrid Modeling
100%
Gene Expression Data
50%
Underlying Network
50%
Biological Ontology
50%
fuzzy c mean
50%
Inference Method
50%
Mathematics
Particle Swarm Optimization
100%
Neural Network
100%
Inference Method
50%
Real Data
50%
Network Model
50%
Underdetermined Problem
50%
Biochemistry, Genetics and Molecular Biology
Regulatory Network
100%
Cell Cycle
50%
Gene Expression Data
50%
Biological Phenomena and Functions Concerning the Entire Organism
50%
Gene Ontology
50%
Chemical Engineering
Recurrent Neural Network
100%
Gene Expression
50%