ECCO: A Python UI for Performing Ensemble Clustering Combined with Cluster Optimization on Omics Data
Year: 2022
Type: preprint
Abstract: Modern biological research often leverages clustering to elucidate disease endotypes and underlying mechanisms. Mono-cluster solutions remain the predominant method; however, this approach has concerning pitfalls, motivating the need for ensemble clustering methods. We present Ensemble Clustering Combined with Cluster Optimization (ECCO), an open-source Python UI that provides a fast, scalable framework for ensemble clustering of large-scale data. It includes zero-code integration of novel ensemble clustering methods and many pre- and post-processing functionalities, enabling researchers to efficiently integrate advanced clustering methodologies into their analysis pipelines.
Authors: Brady David Hislop, Kenna Brown, Hunter Hasskamp, Connor Boone, Mark Greenwood, Chelsea M. Heveran, Ronald K. June
Institutions: Montana State University, University of Washington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1716412532, G2036208247, Role of Glucose metabolism in Chondrocyte Mechanotransduction, Understanding and Manipulating Mechanics-Based Collagen Synthesis to Advance Functional Biomaterials, G6509427819, Metabolomic Profiling to Identify Candidate Biomarker Profiles and Molecular Endotypes for Osteoarthritis
OpenAlex ID: W4308374943