Survey of Natural Language Processing Techniques in Bioinformatics.

Area of Science:

• Bioinformatics
• Computational Biology
• Text Mining
• Natural Language Processing

Background:

• Informatics methods are integral to modern bioinformatics research.
• Text mining and natural language processing (NLP) offer powerful tools for biological data analysis.

Purpose of the Study:

• To explore text mining and NLP methods in bioinformatics.
• To review their applications in knowledge discovery and database reconstruction.
• To discuss future research directions.

Main Methods:

• Literature review and analysis of informatics techniques.
• Application of text mining for extracting biological relationships (e.g., protein-protein interactions, gene-disease links) from databases like PubMed.
• Analysis of NLP applications in predicting protein structure and function, and detecting noncoding RNA.

Main Results:

• Text mining enables efficient retrieval of biological knowledge and reconstruction of databases.
• NLP techniques are successfully applied to complex bioinformatics tasks such as protein structure prediction and noncoding RNA detection.
• Identified numerous methods and applications demonstrating significant contributions to the field.

Conclusions:

• Text mining and NLP are essential for advancing bioinformatics research.
• These informatics approaches facilitate knowledge discovery and drive innovation in biological data analysis.
• Continued development and application of these methods will be crucial for future bioinformatics endeavors.