Knowledge Discovery by Accuracy Maximization

A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the clarity of results in spatially resolved data.

KODAMA

A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. The method facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. It incorporates a novel strategy to integrate spatial information, enhancing the interpretability of results in spatially resolved data.

Citation

Abdel-Shafy EA, Kassim M, Vignol A, et al. (2025). KODAMA enables self-guided weakly supervised learning in spatial transcriptomics. BioRxiv 2025.

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017). KODAMA: an R package for knowledge discovery and data mining. Bioinformatics, 33(4), 621-623.

Cacciatore S, Luchinat C, Tenori L. (2014). Knowledge discovery by accuracy maximization. Proceedings of the National Academy of Sciences, 111(14), 5117-5122.

Installation

This third version of KODAMA will be available soon on https://CRAN.R-project.org/package=KODAMA.

library(devtools)
install_github("tkcaccia/KODAMA")