Entropic imprints on bioinformatics
The entropic framework is a crucial method in statistical inference that helps scientists create models to describe and predict biological systems, particularly complex networks like gene interactions. Its effectiveness comes from its simple concepts and solid mathematical foundation, making it appl...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
REA Press
2024-12-01
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| Series: | Big Data and Computing Visions |
| Subjects: | |
| Online Access: | https://www.bidacv.com/article_205921_cca81190560c3966df7f821a5bfad7aa.pdf |
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| Summary: | The entropic framework is a crucial method in statistical inference that helps scientists create models to describe and predict biological systems, particularly complex networks like gene interactions. Its effectiveness comes from its simple concepts and solid mathematical foundation, making it applicable in various situations. This paper reviews the significant impact of entropy in steering Bioinformatics giant steps ahead through analyzing biological data, such as reconstructing how genes interact and understanding bacterial metabolism and much more. Indeed, the steering hand of entropy in Bioinformatics was arguably in question for many decades until the start of the exploratory era of depicting the phenomenal applicability of entropy in interdisciplinary fields of human knowledge, especially Bioinformatics. Undoubtedly, entropy has a great imprint, which has already changed the way we should think of Bioinformatics based on its multi-faced nature, whether being looked at from an angle of physics, information-theoretic, thermodynamical, chaotic-led approach, and a bird-eye view of a computing perspective. The flow of the current review continues by showcasing the entropic fingerprints on Bioinformatics, resulting in many exceptional discoveries that have enormously added to the existing knowledge. Most importantly, some emerging open problems are provided. Providing these open problems would depict a plethora of issues that need further exploration to build bridges for contemporary Entropic-Bioninformatics Theory. The paper ends with concluding remarks and future research pathways. |
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| ISSN: | 2783-4956 2821-014X |