Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology.
Michael RobbenMohammad Sadegh NasrAvishek DasJai Prakash VeerlaManfred HuberJustyn JaworskiJon WeidanzJacob M. LuberPublished in: J. Comput. Biol. (2023)
Keyphrases
- machine learning algorithms
- strengths and weaknesses
- network topology
- machine learning
- feature selection
- benchmark data sets
- gene ontology
- protein protein interactions
- input features
- learning algorithm
- ad hoc networks
- advantages and disadvantages
- network structure
- decision trees
- statistical machine learning
- machine learning methods
- metabolic pathways
- metadata
- learning problems
- routing protocol
- metabolic networks
- machine learning approaches
- machine learning models
- network topologies
- gene expression data
- active learning
- learning models
- gene selection
- biological pathways
- escherichia coli
- relative strengths and weaknesses
- text categorization
- gene expression
- standard machine learning algorithms
- genome annotation
- signaling pathways
- protein interaction
- gene regulatory networks
- sequence data
- wireless sensor networks
- text classification
- information gain