20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering

It has been demonstrated that the 20D-Dynamic Representation of Protein Sequences, combined with the K-means clustering algorithm, successfully classifies subtypes of influenza A virus strains.

Authors

Dorota Bielińska-Wa̧ż (ORCID: https://orcid.org/0000-0003-4674-0576)
Piotr Wąż (ORCID: https://orcid.org/0000-0002-0696-9950)
Agata Błaczkowska (ORCID: https://orcid.org/0009-0004-9697-5079)

Institutions

Gdańsk Medical University (PL)

Publication Details

Journal: Combinatorial Chemistry & High Throughput Screening
Published: 2025-02-26
DOI: https://doi.org/10.2174/0113862073359729250220131623
Citations: 3
Primary Topic: Machine Learning in Bioinformatics
Type: article
Field-Weighted Citation Impact: 2.42

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article

20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering

Dorota Bielińska-Wa̧ż, Piotr Wąż, Agata Błaczkowska

Feb 26, 2025

3 citations

Combinatorial Chemistry & High Throughput Screening

Machine Learning in Bioinformatics

2.42

article

20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering

Dorota Bielińska-Wa̧ż, Piotr Wąż, Agata Błaczkowska

February 26, 2025article en

3 citations

Abstract

It has been demonstrated that the 20D-Dynamic Representation of Protein Sequences, combined with the K-means clustering algorithm, successfully classifies subtypes of influenza A virus strains.

Combinatorial Chemistry & High Throughput ScreeningVol. 28

Gdańsk Medical University (PL)

Openalex Percentile: Top 13%

Machine Learning in Bioinformatics

2.42

DOI Source OpenAlex

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20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering — Dorota Bielińska-Wa̧ż, Piotr Wąż, et al. · Combinatorial Chemistry & High Throughput Screening (2025) | TGRS Research Map | TGRS