Machine learning techniques to discover genes with potential prognosis role in Alzheimer's disease using different biological sources.
María Martínez-BallesterosJosé Manuel García-HerediaIsabel A. Nepomuceno-ChamorroJosé Cristóbal Riquelme SantosPublished in: Inf. Fusion (2017)
Keyphrases
- dna microarray
- gene sets
- biological entities
- gene function
- regulatory networks
- gene interactions
- gene expression
- candidate genes
- molecular biology
- protein protein interaction networks
- gene expression analysis
- microarray data
- machine learning methods
- molecular level
- biological processes
- differentially expressed genes
- biologically meaningful
- related genes
- gene regulatory networks
- disease genes
- biological data
- gene gene
- machine learning
- gene ontology terms
- saccharomyces cerevisiae
- gene expression patterns
- microarray
- biological knowledge
- microarray expression data
- complex diseases
- drosophila melanogaster
- machine learning algorithms
- signaling pathways
- breast cancer
- systems biology
- outcome prediction
- gene selection
- computer aided diagnosis
- biological systems
- microarray data analysis
- functional genomics
- gene ontology
- biological information
- gene gene interactions
- biomedical literature
- experimental conditions
- functional properties
- interaction networks