MeLSI - Metric Learning for Statistical Inference in Microbiome Analysis
MeLSI (Metric Learning for Statistical Inference) is a novel machine learning method for microbiome data analysis that learns optimal distance metrics to improve statistical power in detecting group differences. Unlike traditional distance metrics (Bray-Curtis, Euclidean, Jaccard), MeLSI adapts to the specific characteristics of your dataset to maximize separation between groups. The method uses an ensemble of weak learners to identify which microbial features drive group differences, providing both improved statistical power and biological interpretability through feature importance weights.
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softwarestatisticalmethodmicrobiomecpp
4.97 score 1 stars 17 scripts 316 downloadswoven - Weighted Omics View Embedding via Nystrom for Incomplete Multi-Omics Data
Supervised multi-omics integration for block-missing ("ragged") data. WOVEN learns a shared latent space across V omics modalities using only fully-observed anchor subjects, then projects block-missing subjects via Nystrom extension without feature-level imputation. Supervision enters through a label-augmented cross-covariance (analogous to DIABLO), and projection matrices can optionally be ridge-regularized and univariately feature-screened for high-dimensional modalities. Designed for comparative effectiveness research where intersection-only methods introduce selection bias.
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softwarestatisticalmethodmultiplecomparisongeneexpressiontranscriptomicsproteomicsmetabolomicsdimensionreductionclusteringclassificationsequencing
3.40 score 3 scripts