The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables 
    non-parametric, asymptotic P-value computation for multivariate linear models. MANTA
    relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are
    computed using a highly accurate approximation of the corresponding cumulative distribution 
    function. Garrido-Martín et al. (2022) <doi:10.1101/2022.06.06.493041>.

The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025) <doi:10.1080/00273171.2025.2503833> and Nehler and Schultze (2026) <doi:10.1037/met0000828>. Deletion-based approaches are also available but play a secondary role.