Integrative hierarchical ensemble clustering for improved disease subtype discovery

Bastian Pfeifer, Andrei Voicu-Spineanu, Michael G Schimek, Nikolaos Alachiotis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Multi-omics clustering methods are used for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. Here, we propose a comprehensive framework for multi-omics hierarchical ensemble clustering. We provide a flexible environment that allows to build hierarchical clustering ensembles suitable for the available data and research goals. Survival analyses for data from The Cancer Genome Atlas (TCGA) indicate that our proposed ensembles provide more robust, and thus more reliable results than the state-of-the-art. We have implemented our architecture within the R-package HC-fused, which is freely available on Github.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Pages720-725
Number of pages6
ISBN (Electronic)978-1-6654-0126-5
DOIs
Publication statusPublished - 14 Jan 2022
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Online Event
Duration: 9 Dec 202112 Dec 2021

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Abbreviated titleBIBM 2021
CityOnline Event
Period9/12/2112/12/21

Fingerprint

Dive into the research topics of 'Integrative hierarchical ensemble clustering for improved disease subtype discovery'. Together they form a unique fingerprint.

Cite this