Work

Vector Representation of Biological Data Using Machine Learning

Year: 2024

Type: conference-paper

Abstract: : The increasing volume of biological data, such as protein-protein interactions (PPI), presents challenges for analysis and interpretation. This work focuses on representing PPI data, traditionally structured in graph format, in vector form. This approach uses advanced dimensionality reduction techniques to visualize high-dimensional data in manageable forms. By converting PPI data into vector form, we enable more effective clustering and identification of functional relationships between proteins, facilitating the discovery of hidden patterns and insights. This work represents a significant advancement in bioinformatics, offering powerful tools for analyzing complex biological systems and facilitating future research and discoveries.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14278568

Authors: Karadimas, O., Karvelis, P., Stylios, C. D.

Institutions: University of Ioannina, Industrial Systems Institute

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W6949563458

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