Detecting evolution of bioinformatics with a content and co-authorship analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Bibliometrics

Background:

• Bioinformatics integrates computational techniques with biological data analysis.
• Bibliometrics analysis, using citation patterns, is a valuable tool for understanding research fields.
• Understanding the knowledge structure of bioinformatics is crucial for its advancement.

Purpose of the Study:

• To explore the knowledge structure of bioinformatics using bibliometrics.
• To analyze trends, content similarity, and co-authorship networks within bioinformatics research.
• To assess the growth, dynamism, and diversification of the bioinformatics field.

Main Methods:

• Data collection from four core bioinformatics journals and four conferences via DBLP.
• TF-IDF term vector conversion for content similarity calculation.
• Co-authorship network analysis for social network similarity and principal component analysis (PCA) for key term extraction and visualization.

Main Results:

• Bioinformatics is identified as a fast-growing, dynamic, and diversified research field.
• Content analysis indicates increasing topic overlap among bioinformatics journals.
• Co-authorship network analysis reveals growing participation from diverse research groups.

Conclusions:

• The study provides insights into the evolving knowledge landscape of bioinformatics.
• The findings highlight the increasing interconnectedness and collaborative nature of bioinformatics research.
• Bibliometrics analysis effectively maps the structure and trends in the bioinformatics field.