Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 
                and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database
                (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
                functional annotation for genomic loci. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.

Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.