multiMiR - Integration of multiple microRNA-target databases with their disease and drug associations
A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR).
Last updated 5 months ago
mirnadatahomo_sapiens_datamus_musculus_datarattus_norvegicus_dataorganismdatamicrorna-sequencesql
8.45 score 20 stars 141 scripts 492 downloadsSmCCNet - Sparse Multiple Canonical Correlation Network Analysis Tool
A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.
Last updated 11 months ago
network
6.40 score 28 stars 30 scripts 215 downloadsMSPrep - Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data
Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data.
Last updated 5 months ago
metabolomicsmassspectrometrypreprocessing
5.20 score 10 stars 4 scripts 362 downloadsMAI - Mechanism-Aware Imputation
A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present.
Last updated 5 months ago
softwaremetabolomicsstatisticalmethodclassificationimputation-methodsmachine-learningmissing-data
5.00 score 2 stars 6 scripts 238 downloadsHeritSeq - Heritability of Gene Expression for Next-Generation Sequencing
Statistical framework to analyze heritability of gene expression based on next-generation sequencing data and simulating sequencing reads. Variance partition coefficients (VPC) are computed using linear mixed effects and generalized linear mixed effects models. Compound Poisson and negative binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143.
Last updated 6 years ago
2.60 score 2 stars 8 scripts 227 downloads