Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying 
    gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 
    'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 
    'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 
    'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 
    'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>),
    'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 
    'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>),
    'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 
    'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 
    'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>).
    Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. 
    The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of 
    terms ID and term description.

Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>).
  Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.

Genetic algorithm are a class of optimization
    algorithms inspired by the process of natural selection and genetics.
    This package is for learning purposes and allows users to optimize
    various functions or parameters by mimicking biological evolution
    processes such as selection, crossover, and mutation. Ideal for tasks
    like machine learning parameter tuning, mathematical function
    optimization, and solving an optimization problem that involves finding 
    the best solution in a discrete space.