preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.1344.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 16 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (16:47:15 CEST)

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

Genome data recognition, Principal component analysis, Louvain algorithm, Cancer dataset.

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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