Pathway Enrichment Analysis Online: A Guide to Web-Based Tools for Non-Programmers

Pathway enrichment analysis identifies biological functions that are overrepresented in a group of genes more than would be expected by chance, then ranks those functions by relevance using statistical methods and online bioinformatics databases. For researchers, students, and life-science professionals who do not write code, web-based tools such as DAVID, Enrichr, WebGestalt, ShinyGO, and STRING provide accessible routes to this analysis. This article explains what these tools do, how to prepare inputs, how to run analyses, and how to interpret outputs without using command-line software.

What Pathway Enrichment Analysis Does

Gene lists are routinely produced from various omic studies, including transcriptomics, proteomics, and genome-wide association studies. Enrichment analysis links these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases. The core question is simple: among the genes you have identified as interesting, which biological pathways or functional categories appear more often than random chance would predict?

The method works by comparing your gene list against curated gene sets that represent known pathways. Databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology, and Reactome store these curated sets. The analysis calculates whether the overlap between your list and each pathway is statistically significant. Tools differ in the statistical tests they use, the databases they query, and the visualizations they produce.

Pathway enrichment analysis has been widely used to identify cancer risk pathways and contributes to elucidating the mechanism of tumorigenesis. However, most existing approaches use pathway information that may be outdated and neglect the complex gene interactions within pathways. A topology-based pathway enrichment analysis method was developed to integrate topological properties and global upstream and downstream positions of genes in pathways, and it identified several well-known cancer risk pathways that could not be obtained by existing tools. This illustrates that the choice of tool and method can affect which pathways you detect.

Why Web-Based Tools Matter for Non-Programmers

The spread of the internet and higher availability of computational resources made pathway enrichment analysis software tools easy to access and use for bioinformatics practitioners worldwide. Web-based tools remove the need to install software, manage dependencies, or write scripts. They accept gene lists in simple text formats and return results in browser-readable tables and graphs.

Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. Nine quick tips for pathway enrichment analysis were proposed to avoid common mistakes and to produce complete, sound, thorough analyses with relevant and robust results. Some tips explain what to do before starting an analysis, others suggest how to correctly generate meaningful results, and final guidelines indicate useful steps to properly interpret results.

The practical implication is that web tools lower the technical barrier but do not remove the need for careful experimental design and interpretation. A researcher who understands the underlying logic of enrichment analysis will obtain more reliable results than one who simply uploads a gene list and accepts the top hits.

At a Glance: Comparison of Web-Based Pathway Enrichment Tools

The table below summarizes the main web-based tools covered in this article. Each tool has different strengths, input requirements, and output formats.

Preparing Your Gene List

Before using any web tool, you need a clean, properly formatted gene list. The quality of your input determines the quality of your output. A gene list with incorrect identifiers, duplicate entries, or mixed species will produce misleading enrichment results.

Identifier Selection

Each tool expects gene identifiers in specific formats. Common formats include official gene symbols, Entrez Gene IDs, Ensembl IDs, and RefSeq accession numbers. DAVID, for example, accepts multiple identifier types but works best with Entrez Gene IDs or official gene symbols. Enrichr accepts gene symbols directly. ShinyGO accepts gene symbols and maps them to its internal annotation database derived from Ensembl and STRING-db.

Check which identifier format your source data uses. RNA-seq pipelines often output Ensembl gene IDs. Microarray platforms may use probe IDs that need conversion. Proteomics studies may report UniProt accession numbers. Most web tools provide identifier conversion utilities, but converting before upload reduces errors.

Species Selection

Select the correct organism for your analysis. Pathway databases are organized by species, and gene identifiers can map to different pathways in different organisms. ShinyGO was built on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archaeal, and 1678 bacterial species. This broad coverage means you can analyze non-model organisms, but you must specify the species correctly.

For species without dedicated pathway annotations, some tools allow analysis using human homologs. One study of ketosis resistance in dairy cattle used Enrichr to perform pathway analysis with human homologs of the identified genes. This approach can reveal conserved pathways but may miss species-specific biology.

Data Cleaning

Remove duplicate genes from your list. Duplicates inflate the apparent size of your gene set and distort statistical calculations. Remove genes with no annotation in the target database. These unannotated genes cannot contribute to enrichment and may reduce statistical power if left in the list.

Check for gene symbol conversions that may have gone wrong. One web tool, STAGEs, was designed to handle Excel gene-to-date misconversions, ensuring that every gene is considered for pathway analysis. Spreadsheet software can convert gene symbols that resemble dates, such as SEP1 or MARCH1, into date formats. If you prepare your gene list in Excel, verify that gene symbols were not altered.

Step-by-Step Workflow for DAVID

DAVID, which stands for Database for Annotation, Visualization and Integrated Discovery, is one of the oldest and most widely used web-based enrichment tools. It integrates functional annotations from multiple sources and provides detailed pathway mapping.

Uploading Your Gene List

Navigate to the DAVID website and select the functional annotation tool. Paste your gene list into the upload box or upload a text file. Select the identifier type that matches your data. Select the species. Submit the list and wait for DAVID to map your identifiers to its internal database.

DAVID will report how many of your submitted genes were successfully mapped. If a large proportion of genes fail to map, check your identifier format and species selection. A mapping rate below 70 percent suggests a formatting problem.

Running Functional Annotation

After successful mapping, select the functional annotation chart option. Choose the annotation categories you want to test, including GO terms, KEGG pathways, and other databases. DAVID will calculate enrichment for each category using a modified Fisher exact test.

The output is a table with columns for the pathway or term name, the number of genes from your list in that category, the total number of genes in that category in the background, and the p-value. DAVID also provides a Benjamini-Hochberg adjusted p-value to control for multiple testing.

Interpreting DAVID Results

Focus on categories with adjusted p-values below your chosen threshold, commonly 0.05. Examine the gene lists within each enriched category to confirm that the enrichment makes biological sense. A pathway enriched for genes related to immune response in a study of autoimmune disease is a plausible finding. The same pathway enriched in a study of muscle development may indicate contamination or misannotation.

DAVID also provides functional annotation clustering, which groups similar terms together. This reduces redundancy in your results and helps identify broader biological themes. One study of systemic lupus erythematosus used DAVID and other online analyses to perform GO and KEGG pathway enrichment of differentially expressed genes between disease and healthy samples.

Step-by-Step Workflow for Enrichr

Enrichr is a web-based tool designed for fast, user-friendly enrichment analysis. It maintains a large collection of gene set libraries and provides interactive visualizations. Enrichr has been used in diverse studies, including the analysis of candidate proteomic biomarkers in systemic sclerosis and the identification of pathways associated with ketosis resistance in dairy cattle.

Uploading Your Gene List

Go to the Enrichr website. Paste your gene list into the input box. Enrichr accepts gene symbols, Entrez IDs, and several other formats. You can also upload a file. Select the gene set libraries you want to test. Enrichr offers dozens of libraries, including KEGG, GO Biological Process, GO Molecular Function, GO Cellular Component, WikiPathways, Reactome, and many disease and drug signature libraries.

For a first analysis, select a few core libraries such as KEGG and GO Biological Process. Adding too many libraries produces a large output that is difficult to interpret.

Running the Analysis

Submit your gene list. Enrichr will run enrichment analysis against the selected libraries and return results within seconds. The results page shows a table for each library with ranked terms, combined scores, p-values, and adjusted p-values.

Enrichr uses a combined score that incorporates the p-value and the z-score of the deviation from the expected rank. Higher combined scores indicate stronger enrichment. The adjusted p-value accounts for multiple testing within each library.

Visualizing Results

Enrichr provides several visualization options. Bar charts show the top enriched terms with colors indicating significance. Network visualizations show connections between enriched terms and your genes. You can export results as tables or images for use in publications.

One study used Enrichr to perform pathway analysis with human homologs of genes associated with ketosis resistance in dairy cattle. The analysis highlighted the acylglycerol homeostasis pathway, the regulation of triglyceride metabolism, and the role of chylomicrons in maintaining metabolic balance during ketosis. Immune response pathways were also linked to the genes associated with ketosis, offering insights into the interplay between metabolic and immune pathways.

Step-by-Step Workflow for WebGestalt

WebGestalt, which stands for Web-based Gene Set Analysis Toolkit, offers multiple enrichment analysis methods in a single platform. It supports overrepresentation analysis, gene set enrichment analysis, and network topology analysis.

Uploading Your Gene List

Go to the WebGestalt website. Select the organism of interest. Choose the method of interest, typically overrepresentation analysis for a simple gene list. Upload your gene list or paste it into the input box. Select the identifier type and the functional database to test.

WebGestalt allows you to select the reference set for the analysis. The default is all genes in the genome for your selected organism. You can also upload a custom reference set if your analysis requires a specific background.

Running the Analysis

Submit the analysis. WebGestalt will calculate enrichment using a hypergeometric test and provide adjusted p-values using the Benjamini-Hochberg method. The results page shows a table of enriched categories with gene counts, expected counts, and enrichment ratios.

WebGestalt also provides visualizations including bar charts, enrichment maps, and pathway diagrams. You can download results in multiple formats.

Interpreting WebGestalt Results

Examine the enrichment ratio, which compares the proportion of your genes in a category to the proportion expected by chance. A high enrichment ratio with a significant adjusted p-value indicates a strong signal. Check the gene lists within each enriched category to verify biological plausibility.

WebGestalt supports gene set enrichment analysis for ranked gene lists, which is useful when you have a full list of genes ranked by differential expression or other statistics instead of a thresholded list of significant genes.

Step-by-Step Workflow for ShinyGO

ShinyGO is a graphical gene-set enrichment tool for animals and plants. It was developed based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archaeal, and 1678 bacterial species. ShinyGO's features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein-protein interaction networks.

Uploading Your Gene List

Go to the ShinyGO website. Paste your gene list into the input box or upload a file. Select the species from the dropdown menu. ShinyGO will map your gene symbols to its internal annotation database.

Running the Analysis

Select the pathway databases you want to test, including GO Biological Process, GO Molecular Function, GO Cellular Component, and KEGG. Submit the analysis. ShinyGO will return enrichment results with fold enrichment values and false discovery rates.

Visualizing Results

ShinyGO provides graphical visualization of enrichment results. Pathway diagrams are retrieved from KEGG, and protein-protein interaction networks are retrieved from STRING. These visualizations help you see how your genes fit into known pathways and interact with each other.

ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene sets. The tool is available at http://ge-lab.org/go/.

Step-by-Step Workflow for STRING

STRING is a database that compiles, scores, and integrates protein-protein association information drawn from experimental assays, computational predictions, and prior knowledge. Its goal is to create comprehensive and objective global networks that encompass both physical and functional interactions. STRING also provides supplementary tools such as network clustering and pathway enrichment analysis.

Uploading Your Gene List

Go to the STRING website. Paste your gene list into the input box or upload a file. Select the organism. STRING will map your identifiers to its protein database.

Running the Analysis

Submit the list. STRING will generate a protein-protein interaction network showing connections between your proteins. The network view includes confidence scores for each interaction. You can adjust the confidence threshold to include or exclude weaker interactions.

Running Enrichment Analysis

STRING provides pathway enrichment analysis as a supplementary tool. The latest version, STRING 12.5, introduced a new regulatory network that gathers evidence on the type and directionality of interactions using curated pathway databases and a fine-tuned language model parsing the literature. This update enables users to visualize and access three distinct network types: functional, physical, and regulatory, separately, each applicable to distinct research needs.

The pathway enrichment detection functionality has been updated with better false discovery rate corrections, redundancy filtering, and improved visual displays. STRING also offers improved annotations of clustered networks and provides users with downloadable network embeddings, which facilitate the use of STRING networks in machine learning and allow cross-species transfer of protein information.

Interpreting STRING Results

Examine the network for clusters of highly connected proteins. These clusters often represent functional modules. The enrichment analysis will show which pathways are overrepresented in your gene list. STRING provides false discovery rate corrections to control for multiple testing.

One study of systemic sclerosis used STRING to assess the interactions of recorded proteins. The analysis showed that some proteins interact, creating significant clusters, while others do not display any evidence of interaction. This highlighted the complexity of the disease and the value of interaction analysis alongside enrichment.

Choosing the Right Tool for Your Question

Different research questions call for different tools. The choice depends on your data type, your species, and the kind of output you need.

Exploratory Analysis

For a quick first look at your gene list, Enrichr is the fastest option. It accepts gene symbols directly, runs in seconds, and provides results across many libraries. The interactive visualizations help you identify the most promising pathways for further investigation.

Detailed Functional Annotation

For a thorough annotation of your gene list with multiple databases, DAVID provides the most detailed output. The functional annotation clustering feature groups related terms and reduces redundancy. DAVID is well suited for generating comprehensive tables for supplementary materials.

Multiple Analysis Methods

For flexibility in analysis methods, WebGestalt supports overrepresentation analysis, gene set enrichment analysis, and network topology analysis in one platform. This is useful when you want to compare results across methods or when you have ranked gene lists.

Non-Model Organisms

For species with limited pathway annotations, ShinyGO provides broad species coverage. The tool includes annotations for hundreds of plant, animal, archaeal, and bacterial species. If your species is not directly supported, you may need to use human homologs as was done in the dairy cattle ketosis study.

Interaction Context

For understanding how your genes interact with each other, STRING provides protein-protein interaction networks alongside enrichment analysis. The network view helps you identify functional modules and hub proteins. The regulatory network in STRING 12.5 adds information about the directionality of regulation.

Common Failure Patterns and How to Avoid Them

Web-based enrichment tools are easy to use but easy to misuse. Several common failure patterns produce misleading results.

Incorrect Identifier Format

The most common error is uploading gene identifiers in a format the tool does not recognize. Each tool documents its accepted formats. Check the documentation before upload. If many genes fail to map, verify your identifier format.

Wrong Species Selection

Selecting the wrong species produces spurious results. Gene symbols can map to different genes in different species. Always verify the species selection before running the analysis.

Unclean Gene Lists

Gene lists with duplicates, date-converted symbols, or unannotated genes produce unreliable results. Clean your list before upload. Remove duplicates and verify that gene symbols were not altered by spreadsheet software.

Ignoring Multiple Testing

Running enrichment against many categories produces many statistical tests. Without multiple testing correction, you will see many false positives. Use adjusted p-values such as the Benjamini-Hochberg correction. Most tools provide these automatically.

Overinterpreting Marginal Results

A pathway with a p-value just below 0.05 may not be biologically meaningful. Consider the effect size, the number of genes involved, and the biological plausibility. Replicate your analysis with an independent gene list if possible.

Using Outdated Pathway Information

Pathway databases are updated regularly. Some tools use outdated pathway information, which can miss recent discoveries. Check the version of the pathway database used by your tool. The topology-based pathway enrichment analysis method was developed partly because existing approaches used outdated pathway information.

Records and Reproducibility

Reproducibility is essential in computational biology. Record the exact parameters used for each analysis so that others can replicate your results.

What to Record

For each enrichment analysis, record the tool name and version, the date of analysis, the gene list file, the identifier type, the species, the pathway databases tested, the statistical method, and the multiple testing correction. Most tools provide a citation or a link to the exact version used.

Exporting Results

Export the full results table, beyond the top hits. The full table includes all tested categories with their statistics. This allows others to see what was tested and what was not significant. Most tools provide download options for tables and images.

Sharing Data

Consider depositing your gene lists and analysis parameters in a public repository. The FAIR Guiding Principles describe the importance of making data findable, accessible, interoperable, and reusable. Following these principles improves the reproducibility of your work and enables others to build on your findings.

Quality Controls and Validation

Several quality controls can improve the reliability of your enrichment analysis.

Background Set Selection

The choice of background set affects enrichment results. The default background is usually all genes in the genome. For some analyses, a more appropriate background is the set of genes measured in your experiment. For example, if your gene list comes from a platform that measures only a subset of genes, use that subset as the background.

Positive and Negative Controls

Test your analysis pipeline with a gene list known to be enriched for a specific pathway. This confirms that the tool is working correctly. Similarly, test with a random gene list to confirm that the tool does not produce spurious enrichment.

Cross-Validation with Multiple Tools

Run the same gene list through two different tools, such as Enrichr and DAVID. Pathways that appear in both results are more likely to be genuine. Pathways that appear in only one tool may be artifacts of that tool's annotation database or statistical method.

Manual Review of Enriched Genes

For each enriched pathway, examine the specific genes from your list that contribute to the enrichment. Confirm that these genes make biological sense in the context of your study. A pathway enriched for genes that are unrelated to your experimental system may indicate a problem.

Limitations of Web-Based Tools

Web-based tools have limitations that you should understand before relying on them for critical decisions.

Annotation Completeness

Pathway databases are incomplete. Many genes have no pathway annotation, and many pathways are not fully characterized. Enrichment analysis can only detect enrichment for pathways that are in the database. A lack of enrichment does not mean a lack of biological relevance.

Statistical Assumptions

Enrichment analysis assumes that genes are independent, but genes within pathways are often co-regulated. This can inflate significance estimates. Some tools, such as netGO, integrate network information to address this limitation. netGO implements a framework that integrates both the overlap between target genes and pathway gene sets and the network interactions between them, and it adaptively resamples genes based on network degrees to assess pathway enrichment.

Identifier Mapping Loss

Some genes in your list may not map to the tool's annotation database. This reduces the effective size of your gene list and can reduce statistical power. Check the mapping rate and consider whether the unmapped genes are biologically important.

Species Limitations

Not all species have comprehensive pathway annotations. For non-model organisms, you may need to use human homologs, which can miss species-specific biology. ShinyGO provides broad species coverage, but even this tool cannot cover every species.

Version Differences

Pathway databases change over time. Results obtained with one version may differ from results obtained with another version. Record the version used and cite it in your methods.

Safety and Regulatory Context

Pathway enrichment analysis is a computational method and does not involve direct safety risks to humans or animals. However, the results can inform decisions with safety implications.

Clinical and Diagnostic Context

If your analysis informs clinical decisions, such as identifying biomarkers for disease diagnosis, the results must be validated with appropriate experimental methods. One study of systemic lupus erythematosus used enrichment analysis to identify differentially expressed genes and then conducted receiver operating characteristic analyses to explore the value of the genes for diagnosis. Computational predictions alone are not sufficient for clinical use.

Genomic Data Sharing

If your gene lists are derived from human subjects, you must comply with applicable data sharing policies. The National Institutes of Health Genomic Data Sharing Policy describes expectations for sharing genomic data generated from NIH-funded research. Review the policy at https://sharing.nih.gov/genomic-data-sharing-policy before sharing any human genomic data.

Data Management

Follow the FAIR Guiding Principles for scientific data management and stewardship. These principles emphasize that data should be findable, accessible, interoperable, and reusable. The principles were published in Scientific Data and are available at https://doi.org/10.1038/sdata.2016.18.

Professional Escalation Criteria

Some situations warrant consultation with a bioinformatics specialist or statistician.

Persistent Identifier Mapping Failures

If more than 30 percent of your genes fail to map in multiple tools, you may have an identifier problem that requires specialized conversion tools. A bioinformatics specialist can help resolve identifier mapping issues.

Conflicting Results Across Tools

If two tools produce substantially different results for the same gene list, the discrepancy may indicate a problem with your gene list, the tool configurations, or the underlying databases. Consult a specialist to diagnose the issue.

Complex Experimental Designs

If your study involves multiple conditions, time points, or interaction effects, standard enrichment analysis may not capture the relevant biology. Specialized methods such as gene set enrichment analysis for ranked lists or network-based methods may be needed.

Regulatory or Clinical Use

If your analysis will inform regulatory submissions or clinical decisions, consult with a qualified bioinformatics professional to ensure that the methods meet applicable standards. Computational results used in clinical contexts require rigorous validation.

Frequently Asked Questions

What is the difference between overrepresentation analysis and gene set enrichment analysis?

Overrepresentation analysis tests whether a list of significant genes contains more genes from a pathway than expected by chance. Gene set enrichment analysis uses a ranked list of all measured genes and tests whether genes from a pathway are concentrated at the top or bottom of the ranking. Overrepresentation analysis requires you to choose a significance threshold for your gene list. Gene set enrichment analysis does not require a threshold and can detect subtle shifts in pathway activity.

Which web tool is best for beginners?

Enrichr is the most accessible tool for beginners. It accepts gene symbols directly, runs quickly, and provides clear visualizations. The results are easy to interpret, and the tool includes many gene set libraries. DAVID is also suitable for beginners but requires more attention to identifier format and species selection.

Can I use these tools for non-model organisms?

ShinyGO provides the broadest species coverage among the tools discussed, with annotations for hundreds of plant, animal, archaeal, and bacterial species. For species without direct annotations, you can use human homologs of your genes, as was done in a study of ketosis resistance in dairy cattle. This approach can identify conserved pathways but may miss species-specific biology.

How should I choose the background set for enrichment analysis?

The default background is usually all genes in the genome for your selected organism. A more appropriate background may be the set of genes measured in your experiment. For example, if your microarray platform measures only a subset of genes, use that subset as the background. WebGestalt allows you to upload a custom background set.

What does an adjusted p-value mean in enrichment analysis?

An adjusted p-value accounts for multiple testing. When you test thousands of pathways, some will appear significant by chance. The adjusted p-value controls the false discovery rate, which is the expected proportion of false positives among the significant results. Use adjusted p-values instead of raw p-values when interpreting your results.

Why did my gene list produce no significant enrichment?

Several reasons can explain a lack of significant enrichment. Your gene list may be too small to achieve statistical power. Your genes may not be concentrated in any known pathway. The pathway databases may not cover the biology relevant to your study. Or your gene list may contain genes from diverse pathways with no single dominant theme.

Can I use these tools for proteomics data?

Yes. Pathway enrichment analysis works with gene lists derived from proteomics studies. One study of systemic sclerosis performed in silico analysis of mass spectrometry-based candidate proteomic biomarkers using Enrichr. The analysis extracted several pathways associated with different stages of disease pathogenesis. Ensure that your protein identifiers are converted to the gene identifier format expected by the tool.

How do I cite these tools in my publications?

Each tool provides citation guidance on its website. Generally, you should cite the primary publication describing the tool and the version you used. For example, ShinyGO was published in Bioinformatics in 2020, and STRING was published in Nucleic Acids Research in 2025. Check the tool website for the current citation recommendation.

Related Bioinformatics Guides

• Gene Ontology (GO) and Enrichment Analysis
• The KEGG Database and Pathway Analysis
• Codon Usage Bias Analysis for Recombinant Vaccine Design
• Bioinformatics Analysis of Viral Codon Usage Bias and Host Adaptation
• Predicting Vaccine Escape Mutations Using Structure-Based Deep Learning

References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• ShinyGO: a graphical gene-set enrichment tool for animals and plants.. Bioinformatics (Oxford, England), 2020.
• Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis.. Journal of translational medicine, 2021.
• The STRING database in 2025: protein networks with directionality of regulation.. Nucleic acids research, 2025.
• Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay.. Briefings in bioinformatics, 2024.
• Nine quick tips for pathway enrichment analysis.. PLoS computational biology, 2022.
• Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.. Briefings in bioinformatics, 2019.
• netGO: R-Shiny package for network-integrated pathway enrichment analysis.. Bioinformatics (Oxford, England), 2020.
• High-fat diet score reveals total and cardiovascular mortality and identifies protein biomarkers in large cohorts.. Journal of advanced research, 2026.
• Optimizing vasectomy care pathways: patient preparation, follow-through, and evidence-based timing of post-vasectomy semen analysis.. 2026.
• The varieties of integration processes: Four pathways following psychedelic and other exceptional experiences. 2026.
• Assessing cost-effectiveness in oncology treatment sequences: a review of pathway modeling methods for health technology assessment.. 2026.
• Patient and staff acceptability of an artificial intelligence software to prioritize chest X-rays in an NHS setting: a qualitative evaluation.. 2026.
• Reduced inflammatory and Th1 transcriptional profiles in geriatric versus adult cotton rats infected with respiratory syncytial virus.. 2026.
• Gene Enrichment and Pathway Analysis for Ketosis Resistance in Dairy Cattle: A GWAS-Based Approach. Türk Tarım ve Doğa Bilimleri Dergisi, 2024.
• Enrichr in silico analysis of MS-based extracted candidate proteomic biomarkers highlights pathogenic pathways in systemic sclerosis. Scientific Reports, 2023.
• STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies. Scientific Reports, 2023.
• lncRNA PAN3-AS1 Modulates Cilium Assemble Signaling Pathway Through Regulation of RPGR as a Potential MS Diagnostic Biomarker: Integrated Systems Biology Investigation. Journal of Molecular Neuroscience, 2025.
• RGS2-Related Non-coding Interaction Network Modulates the NF-Kappa B Signaling Pathway in MS Patients: A Systems Biology Investigation. Journal of Molecular Neuroscience, 2025.
• A Parallel Software Platform for Pathway Enrichment. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020.
• PAGED: A pathway and gene-set enrichment database to enable molecular phenotype discoveries. BMC Cancer, 2016.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.