Evidence map›Paper›PMID 38759052›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Zongjin Li, Changxin Song, Jiyu Yang, Zeyu Jia, Dongzhen Chen, Chengying Yan, Liqin Tian, Xiaoming Wu

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Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Zongjin LiDepartment of Computer, Qinghai Normal University, Xining, China.
Changxin SongDepartment of Mechanical Engineering and Information, Shanghai Urban Construction Vocational College, Shanghai, China.
Jiyu YangDepartment of Cardiovascular Medicine, Xining First People's Hospital, Xining, China.
Zeyu JiaDepartment of Computer, Qinghai Normal University, Xining, China.
Dongzhen ChenSchool of Materials Science and Engineering, Xi'an Polytechnic University, Xi'an, China.
Chengying YanDepartment of Cardiovascular Medicine, Xining First People's Hospital, Xining, China.
Liqin TianDepartment of Computer, Qinghai Normal University, Xining, China.
Xiaoming WuThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSelecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions.

objectiveThis study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method.

methodsA new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes.

resultsExperiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques.

conclusionsDINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

Indexed as

AlgorithmsGene Expression ProfilingCluster AnalysisComputational BiologyHumansClusteringco-expression modulegene expression datasimilarity measure

Identifiers

PMID38759052
PMCPMC11191479

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