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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

4. Computational Linguistics and Biological Sequences in Artificial Intelligence

verfasst von : Qingfeng Chen

Erschienen in: Association Analysis Techniques and Applications in Bioinformatics

Verlag: Springer Nature Singapore

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Abstract

Researchers generally believe that nucleic acid is also a language with rich information. Nucleic acid language can be used to describe the structure of life and life processes, and there is as much diversity as language, with many common characteristics. Therefore, many existing studies apply the results and methods achieved in the field of language theory to the study of biological sequences. Based on this route, computational linguistics has also brought many new breakthroughs to the study of biological sequences. Association analysis is a data mining technique that can be used to discover frequent patterns and association rules in a dataset. In computational linguistics, association analysis can be used to uncover association rules in text data, helping us better understand semantic and grammatical rules in natural languages. In biological sequence analysis, association analysis can be applied to identify association rules in the genome and proteome to reveal interactions and functional relationships between genes or proteins. These analysis results can help us better understand the data and draw conclusions and promote further development in both fields. Therefore, the application of association analysis technology to the study of biological sequences in computational linguistics is a research field worthy of our expectations.

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Vorheriges Kapitel Complex Networks: Basic Concepts, Construction, and Learning Methods
Nächstes Kapitel Non-Coding RNA Function and Structure
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Metadaten
Titel
Computational Linguistics and Biological Sequences in Artificial Intelligence
verfasst von
Qingfeng Chen
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Association Analysis Techniques and Applications in Bioinformatics

Print ISBN: 978-981-9982-50-9

Electronic ISBN: 978-981-9982-51-6

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-8251-6_4

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