DI2: prior-free and multi-item discretization of biological data and its applications.

ABSTRACT:

Background

A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations.

Results

In this study, an unsupervised discretization method, DI2, for va

SUBMITTER: Alexandre L 

PROVIDER: S-EPMC8425008 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature