A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.

A method for finding enrichments of somatic single nucleotide variants (SNVs) and small insertions-deletions (Indels) in functional elements in the human genome. 'ActiveDriverWGS' detects coding and noncoding cancer driver elements using whole genome sequencing data. The method is part of the publication H. Zhu et al. (2020) <doi:10.1016/j.molcel.2019.12.027> "Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks" in Molecular Cell.

Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) 
    <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. 
    The package provides functions to (i) simulate data streams with true latent states and 
    multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) 
    using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based 
    active learning that balances exploration and exploitation to decide whether to request labels 
    in real time. The methodology is particularly suited for statistical process monitoring 
    in industrial applications where labeling is costly.

Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.