ArticleEntropy (Basel, Switzerland)2022
Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- DL-GapFilling: a novel deep learning framework for improved plant genome gap filling.Briefings in bioinformatics · 2026Article
- Bioinformatics tools for the sequence complexity estimates.Biophysical reviews · 2023Review
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7 authors.
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Abstract
In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index
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