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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).

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mirnadatahomo_sapiens_datamus_musculus_datarattus_norvegicus_dataorganismdatamicrorna-sequencesql

8.59 score 25 stars 187 scripts 1.7k downloads

SmCCNet - Sparse Multiple Canonical Correlation Network Analysis Tool ('SmCCNet')

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. Methods are described in Shi et al. (2019) "Unsupervised discovery of phenotype-specific multi-omics networks" <doi:10.1093/bioinformatics/btz226>.

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network

6.68 score 32 stars 33 scripts 386 downloads

MAI - 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.

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softwaremetabolomicsstatisticalmethodclassificationimputation-methodsmachine-learningmissing-data

5.04 score 2 stars 11 scripts 465 downloads