Details

Autor(en) / Beteiligte

• Shi, Xiuchao
• Yue, Chunxiao
• Quan, Meiping
• Li, Yalin
• Nashwan Sam, Hiba

Titel

A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications

Ist Teil von

• Journal of cancer research and clinical oncology, 2024-01, Vol.150 (1), p.3-3, Article 3

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Introduction In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. Objective Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. Methodology The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. Results The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between “basophils” and “ciliary.epithelial.cells” with 62,809 promoters. Conclusion The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.