Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches

Pınar Karadayı Ataş ()
Additional contact information
Pınar Karadayı Ataş: Department of Computer Engineering, Faculty of Engineering, Istanbul Arel University, 34537 Istanbul, Turkey

Mathematics, 2024, vol. 12, issue 2, 1-26

Abstract: Polycystic ovary syndrome (PCOS) and endometrial carcinoma (EC) are gynecological conditions that have attracted significant attention due to the higher prevalence of EC in patients with PCOS. Even with this proven association, little is known about the complex molecular pathways that connect PCOS to an increased risk of EC. In order to address this, our study presents two main innovations. To provide a solid basis for our analysis, we have first created a dataset of genes linked to EC and PCOS. Second, we start by building fixed-size ensembles, and then we refine the configuration of a single clustering algorithm within the ensemble at each step of the hyperparameter optimization process. This optimization evaluates the potential performance of the ensemble as a whole, taking into consideration the interactions between each algorithm. All the models in the ensemble are individually optimized with the suitable hyperparameter optimization method, which allows us to tailor the strategy to the model’s needs. Our approach aims to improve the ensemble’s performance, significantly enhancing the accuracy and robustness of clustering outcomes. Through this approach, we aim to enhance our understanding of PCOS and EC, potentially leading to diagnostic and treatment breakthroughs.

Keywords: machine learning; molecular biology; mathematical modeling; bioinformatics; PCOS; endometrial cancer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/2/295/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/2/295/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:2:p:295-:d:1320491

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().