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2000
Volume 32, Issue 37
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

A planktonic population of bacteria can form a biofilm by adhesion and colonization. Proteins known as “adhesins” can bind to certain environmental structures, such as sugars, which will cause the bacteria to attach to the substrate. Quorum sensing is used to establish the population is dense enough to form a biofilm. This paper presents a comprehensive overview of our investigation into these processes, specifically focusing on , an emerging pathogen of increasing clinical relevance. In our study, we detailed the methodology employed for the proteomic analysis of , as well as our innovative application of Generative Adversarial Networks (GANs). These advanced computational tools allow us to analyze complex data sets and identify patterns that might otherwise remain obscured. With a particular focus on the effectiveness of GAN, the identified proteins and their potential roles in the context of 's pathogenesis were discussed. The insights gained from this study can significantly contribute to our understanding of this emerging pathogen and pave the way for developing targeted interventions, potentially leading to improved diagnostic tools and more effective therapeutic strategies against infection. The authors achieved 95.43% accuracy for the generator and 87.89% for the discriminator. The model was validated by considering different Machine learning algorithms, reinforcing that integrating computational techniques with microbiological investigations can significantly enhance our understanding of emerging pathogens. Overall, this study emphasizes the importance of exploring the molecular mechanisms behind biofilm formation and pathogenicity, providing a foundation for future research that could lead to innovative solutions in combating infections caused by and other similar pathogens.

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Computational Model to Predict Potential Therapeutic Targets Employing Generative Adversarial Networks for Analysis of Proteins Involved in Mycobacterium fortuitum Biofilm Formation
Curr. Med. Chem. 32, 8336 (2025); https://doi.org/10.2174/0109298673345515241122024326
/content/journals/cmc/10.2174/0109298673345515241122024326
/content/journals/cmc/10.2174/0109298673345515241122024326
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  • Article Type:     Review Article
Keyword(s): biofilm formation; generative adversarial networks; machine learning techniques; Mycobacterium fortuitum; proteomic analysis; quorum sensing

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