Work

Protein Sequence Estimation Using Natural Language Processing Technique

Year: 2026

Type: article

Abstract: Proteins play an important role in various biological processes, and understanding protein sequences is essential for applications such as disease research, drug discovery, and molecular biology. Traditional bioinformatics methods for protein sequence analysis are computationally expensive and require significant processing time. This paper presents a protein sequence estimation approach using Natural Language Processing (NLP) models, including ProtBERT, ProtT5, and Ankh3. These transformer-based models learn contextual relationships between amino acids and enable efficient protein sequence prediction. The proposed approach aims to improve prediction accuracy while reducing computational complexity compared to conventional methods. By combining NLP with bioinformatics, this work provides a simple and scalable solution for protein sequence analysis and supports future research in computational biology.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20937454

Authors: Shaikh, Nehal Amanulla, Tare, Ankush Ladak, Tare, Ramesh Ladak, Dongardive, Sumit Dhanraj, Sale, Deepali

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W7166046199

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