clinical

<p>The data belong to a cohort of 35 patients with prostate cancer from two different hospitals.</p>

datasets

KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 <DOI:10.1073/pnas.1220873111>. Addition functions was introduced by Cacciatore et al. 2017 <DOI:10.1093/bioinformatics/btw705> to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.

Stefano Cacciatore

clinical: Clinical Data of a Cohort of Prostate Cancer Patiens

Description

Usage

Arguments

Value

Examples