Using topic modeling to uncover hidden patterns in Persian bioinformatics publications
Imagine trying to track every conversation at a massive scientific conference with thousands of participants—all speaking simultaneously. That's the challenge researchers face in today's era of rapid scientific production, where new papers are published faster than anyone can read them.
Now consider this problem within specialized fields like bioinformatics, which combines biology, computer science, and information technology to make sense of biological data. How can we identify emerging trends, spot research gaps, or understand the scientific priorities of an entire nation's research community?
This exact challenge inspired a fascinating study that used artificial intelligence to analyze nearly a decade of bioinformatics research by Iranian scientists. By applying sophisticated computational techniques to thousands of research publications, researchers have uncovered the hidden patterns and priorities in Persian bioinformatics research, creating what might be considered the first "research DNA" of this evolving scientific field 1 3 .
Estimated growth of bioinformatics publications over time
At the heart of this analysis lies a powerful technique called topic modeling. In simple terms, topic modeling is a type of machine learning that automatically scans large collections of documents to discover recurring themes or "topics" without human intervention 2 .
Think of it as an extremely diligent research assistant who reads thousands of papers simultaneously, tallying which words frequently appear together, then grouping these words into thematic clusters.
Gather thousands of research papers and abstracts
Clean and prepare text data for analysis
Algorithm identifies word co-occurrence patterns
Groups of related words form distinct topics
The most common approach, called Latent Dirichlet Allocation (LDA), operates on a simple but powerful principle: each document discusses multiple topics, and each topic is represented by a collection of words that frequently appear together 2 . For example, if the words "protein," "sequence," and "structure" often appear in the same documents, the algorithm would group them into a topic that might represent "Molecular Modeling." Similarly, "gene," "expression," and "cancer" might form a "Cancer Bioinformatics" topic 1 .
Topic modeling captures this richness by allowing documents to belong to multiple topics simultaneously, much like how our own conversations naturally span multiple subjects 2 6 .
Bioinformatics emerged in the early 2000s as a response to the explosive growth of biological data generated by genome sequencing technologies 1 . Today, it stands as a mature scientific discipline essential to areas ranging from drug development to personalized medicine. Iranian researchers have actively participated in this global scientific endeavor, but until recently, no systematic analysis existed to map their specific contributions and research priorities.
The significance of understanding a nation's research profile extends beyond academic curiosity. Research priorities reflect national health challenges, available expertise, investment patterns, and can inform future science policy decisions. For a field as resource-intensive as bioinformatics, such analysis helps identify strengths to build upon and gaps that need addressing 1 4 .
Previous studies have analyzed bioinformatics research in various contexts, but none had specifically examined the Persian research landscape using advanced text mining approaches. This gap prompted researchers to conduct a comprehensive analysis of Iranian bioinformatics publications, creating what would become the first detailed map of this evolving scientific territory 1 .
Comprehensive analysis of Iranian bioinformatics publications
When researchers analyzed 3,875 Iranian bioinformatics papers indexed in the Scopus database up to March 2022, the results revealed a clearly structured research landscape consisting of seven dominant topics 1 3 .
| Rank | Word | TF-IDF Weight |
|---|---|---|
| 1 | mir | 105.24 |
| 2 | expression | 85.47 |
| 3 | cancer | 83.80 |
| 4 | vaccine | 82.22 |
| 5 | protein | 78.15 |
TF-IDF identifies distinctive rather than just frequent words 1
Networks, interactions, dynamics
Protein, structure, docking
Expression, miRNA, regulation
Cancer, tumor, therapy
Vaccine, epitope, immunity
Biomarker, diagnostic, detection
COVID-19, SARS-CoV-2, pandemic
The largest cluster represents a holistic approach to understanding biological systems. Unlike traditional reductionist methods that study individual components in isolation, systems biology examines complex interactions within biological networks 4 .
The prominence of this topic among Iranian researchers suggests their engagement with cutting-edge approaches that integrate biology with data science and artificial intelligence.
This field showed a particularly high growth trend, especially notable since 2019. This field applies computational approaches to immunology, with important applications in vaccine design 1 .
The timing of this growth spike coincides with the COVID-19 pandemic, suggesting that Iranian researchers rapidly applied their expertise to address this global health crisis.
Continues to be a major focus, reflecting both the global importance of oncology research and the specific health priorities within Iran. The high TF-IDF weight for "cancer" confirms the distinctive role of cancer-related research within Persian bioinformatics 1 .
While the smallest cluster, represents one of the most timely research areas. The appearance of this topic demonstrates how Iranian bioinformatics researchers pivoted to address the pandemic, using computational tools to study SARS-CoV-2 proteins, genome evolution, and potential treatments 1 .
The process of mapping the Persian bioinformatics research landscape followed a meticulous, multi-stage methodology that transformed raw text into meaningful patterns 1 :
Researchers gathered 3,899 papers indexed in Scopus with Iranian affiliation, using a comprehensive search strategy to capture the full breadth of bioinformatics research.
The abstracts and titles of these papers underwent extensive cleaning, including removal of punctuation, tokenization, and lemmatization.
The cleaned text was converted into numerical format using TF-IDF (Term Frequency-Inverse Document Frequency), which weights words by distinctiveness.
The LDA algorithm was applied to discover latent topics within the document collection, implemented using Python libraries in the Google Colab environment.
Modern bioinformatics research relies on a sophisticated array of computational tools and resources. The following tools are particularly relevant for Persian bioinformatics and text mining research:
Term Frequency-Inverse Document Frequency
Identifies distinctive words in documents 1
Statistical MeasureSpecialized Tool
Extracts and processes Persian Wikipedia bioinformatics content 5
Persian ToolMultiple Kernel Fuzzy Topic Modeling
Handles sparsity and redundancy in biomedical text 6
Advanced AlgorithmThe successful application of topic modeling to Persian bioinformatics research demonstrates the power of computational approaches to illuminate the structure and evolution of scientific fields. The findings offer practical value for:
Perhaps most excitingly, this study represents just the beginning of what's possible with computational research analysis. Emerging approaches like Multiple Kernel Fuzzy Topic Modeling (MKFTM) promise to handle the sparsity and redundancy challenges particularly prevalent in biomedical texts 6 . The integration of large language models specifically trained on Persian biomedical text—such as BioPars—opens new possibilities for more sophisticated analysis of Iran's scientific output 9 .
The application of topic modeling to Persian bioinformatics research has revealed a dynamic, evolving scientific landscape with distinct research priorities and promising growth trajectories. From the holistic perspective of systems biology to the targeted approaches of immunoinformatics and cancer research, Iranian scientists have established a diverse and sophisticated research portfolio.
This analysis does more than just catalog research topics—it reveals a scientific community actively engaging with global challenges while developing tools and approaches suited to their specific linguistic and cultural context. As these efforts continue to mature, supported by both international methodologies and Persian-specific resources, the future of Persian bioinformatics appears bright, diverse, and increasingly impactful.
Perhaps the greatest lesson from this analytical exercise is that science, when viewed through the lens of modern computational tools, reveals itself as a deeply human endeavor—full of patterns, priorities, and conversations that await only the proper tools to make them visible to all.