Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy

Bonidia, Robson Parmezan; Avila-Santos, Anderson P.; Almeida, Breno Lívio Silva de; Stadler, Peter F.; Rocha, Ulisses Nunes da; Sanches, Danilo Sipoli; Carvalho, André C. P. L. F. de

doi:10.3390/e24101398

Cited by 3 publications

(1 citation statement)

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“…This algorithm is based on two parameters, namely q V and q A ; if q V = q A = 1, the standard simulated annealing (SA), also referred to as Boltzmann machine, is recovered. Another set of examples concern machine learning, deep learning and related techniques [128][129][130][131][132][133].…”

Section: Pdfmentioning

confidence: 99%

Non-additive entropies and statistical mechanics at the edge of chaos: a bridge between natural and social sciences

Tsallis

2023

Phil. Trans. R. Soc. A.

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The Boltzmann–Gibbs (BG) statistical mechanics constitutes one of the pillars of contemporary theoretical physics. It is constructed upon the other pillars—classical, quantum, relativistic mechanics and Maxwell equations for electromagnetism—and its foundations are grounded on the optimization of the BG (additive) entropic functional S BG = − k ∑ i p i ln ⁡ p i . Its use in the realm of classical mechanics is legitimate for vast classes of nonlinear dynamical systems under the assumption that the maximal Lyapunov exponent is positive (currently referred to as strong chaos ), and its validity has been experimentally verified in countless situations. It fails however when the maximal Lyapunov exponent vanishes (referred to as weak chaos ), which is virtually always the case with complex natural, artificial and social systems. To overcome this type of weakness of the BG theory, a generalization was proposed in 1988 grounded on the non-additive entropic functional S q = k ( ( 1 − ∑ i p i q ) / ( q − 1 ) ) ( q ∈ R ; S 1 = S BG ) . The index q and related ones are to be calculated, whenever mathematically tractable, from first principles and reflect the specific class of weak chaos. We review here the basics of this generalization and illustrate its validity with selected examples aiming to bridge natural and social sciences. This article is part of the theme issue ‘Thermodynamics 2.0: Bridging the natural and social sciences (Part 2)’.

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Section: Pdfmentioning

confidence: 99%

Non-additive entropies and statistical mechanics at the edge of chaos: a bridge between natural and social sciences

Tsallis

2023

Phil. Trans. R. Soc. A.

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Bioinformatics tools for the sequence complexity estimates

Orlov,

Orlova

2023

Biophys Rev

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BioAutoML: Democratizing Machine Learning in Life Sciences

Bonidia,

Carvalho

2024

Anais Estendidos Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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Recent technological advances have allowed an exponential expansion of biological sequence data, and the extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge improved the understanding of the mechanisms related to several fatal diseases, e.g., Cancer and COVID-19, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine, and precision medicine. These advances benefit our society and economy, directly impacting people’s lives in various areas, such as health care, drug discovery, forensic analysis, and food analysis. Nevertheless, ML approaches to biological data require representative, quantitative, and informative features. Necessarily, as many ML algorithms can handle only numerical data, sequences need to be translated into a feature vector. This process, known as feature extraction, is a fundamental step for elaborating high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with the design and selection of suitable features. Feature engineering, ML algorithm selection, and hyperparameter tuning are often time-consuming processes that require extensive domain knowledge and are performed by a human expert. To deal with this problem, we developed a new package, BioAutoML, which automatically runs an end-to-end ML pipeline. BioAutoML extracts numerical and informative features from biological sequence databases, automating feature selection, recommendation of ML algorithm(s), and tuning of hyperparameters, using Automated ML (AutoML). Our experimental results demonstrate the robustness of our proposal across various domains, such as SARS-CoV-2, anticancer peptides, HIV sequences, and non-coding RNAs. BioAutoML has a high potential to significantly reduce the expertise required to use ML pipelines, aiding researchers in combating diseases, particularly in low- and middle-income countries. This initiative can provide biologists, physicians, epidemiologists, and other stakeholders with an opportunity for widespread use of these techniques to enhance the health and well-being of their communities.

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Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy

Cited by 3 publications

References 58 publications

Non-additive entropies and statistical mechanics at the edge of chaos: a bridge between natural and social sciences

Non-additive entropies and statistical mechanics at the edge of chaos: a bridge between natural and social sciences

Bioinformatics tools for the sequence complexity estimates

BioAutoML: Democratizing Machine Learning in Life Sciences

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