Summary of A Review on the Applications Of Transformer-based Language Models For Nucleotide Sequence Analysis, by Nimisha Ghosh et al.
A Review on the Applications of Transformer-based language models for Nucleotide Sequence Analysis
by Nimisha Ghosh, Daniele Santoni, Indrajit Saha, Giovanni Felici
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces recent advancements in Transformer-based models for natural language processing and explores their potential applications in bioinformatics. By drawing parallels between biological sequences and natural languages, researchers have successfully extended and adapted NLP models for tasks such as nucleotide sequence analysis. The study reviews and analyzes numerous papers on this topic, highlighting key features and approaches for customizing these powerful computational machines. Additionally, the paper provides a structured overview of Transformer functioning, making it accessible to first-time users. This review aims to facilitate understanding of the various applications of Transformer-based language models in bioinformatics, inspiring readers to build upon these methodologies to tackle other problems in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models called Transformers to help scientists analyze and understand DNA sequences. Just like how computers can process natural languages, they can also process biological sequences! The researchers looked at lots of previous studies that used these Transformer models for this task and identified what made them work well or not so well. They also explained in simple terms how these complex computer architectures work. This review will help scientists understand how to use these powerful tools to solve problems in bioinformatics. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Transformer
