Clustering on hierarchical heterogeneous data with prior pairwise relationships.

ABSTRACT:

Background

Clustering is a fundamental problem in statistics and has broad applications in various areas. Traditional clustering methods treat features equally and ignore the potential structure brought by the characteristic difference of features. Especially in cancer diagnosis and treatment, several types of biological features are collected and analyzed together. Treating these features equally fails to identify the heterogeneity of both data structure and cancer itself, which leads to incompleteness and inefficacy of current anti-cancer therapies.

Objectives

In this paper, we propose a clustering framework based on hierarchical heterogeneous data with prior pairwise relationships. The proposed clustering method fully characterizes the difference of features and identifie

SUBMITTER: Han W 

PROVIDER: S-EPMC10807103 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature