Affiliation:
1. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine Taipei Medical University Taipei Taiwan
2. AIBioMed Research Group Taipei Medical University Taipei Taiwan
3. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan
4. Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan
Abstract
AbstractIn recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer‐based NLP models have gained significant attention for their ability to process variable‐length input sequences in parallel, using self‐attention mechanisms to capture long‐range dependencies. In this review paper, we discuss the recent advancements in transformer‐based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer‐based NLP models to revolutionize proteome bioinformatics.
Funder
Ministry of Science and Technology, Taiwan
Subject
Molecular Biology,Biochemistry
Cited by
14 articles.
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