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Daily Arxiv

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A Comprehensive Review of Transformer-based language models for Protein Sequence Analysis and Design

Created by
  • Haebom

Author

Nimisha Ghosh, Daniele Santoni, Debaleena Nawn, Eleonora Ottaviani, Giovanni Felici

Outline

This paper discusses recent advances in the application of Transformer-based language models, which have achieved remarkable results in natural language processing (NLP), to the field of bioinformatics. In particular, it focuses on protein sequence analysis and design, and analyzes and presents strengths and weaknesses of studies in various application areas such as gene ontology, functional and structural protein identification, novel protein production, and protein binding. It points out the shortcomings of existing studies and suggests future research directions, and aims to help researchers in the field to grasp the latest research trends and design future research.

Takeaways, Limitations

Takeaways: It confirms that Transformer-based models have various potential applications in the fields of protein sequence analysis and design, and contributes to the development of the field by suggesting future research directions.
Limitations: The analysis may be biased towards specific studies and miss the overall flow. Also, since the paper is in review form, it does not present new research results. Finally, the explanation of specific ____T2096_____ and directions for improvement may be somewhat lacking.
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