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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Genomic Insight-Engine: An Explainable Rule-Based Framework for Profiling Human Potentials from SNP Data

Gangula Nithya Sree
Student, Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, India.
Muntha Raju
Professor, Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, India.
Yerram Sneha
Assistant Professor, Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, India.
Sirisha Veluri
Assistant Professor, Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, India.

Keywords: Genomic Signatures, Explainable AI, Rule-Based Framework, Human Trait Prediction, Bioinformatics.

Abstract

The skyrocketing development in the field of personal genomics has produced enormous amounts of genetic information yet the issue of how to process this raw material into identifiable and interpretable outcomes is not truly figured out yet. In this paper, the authors present the Genomic Insight-Engine, a rule-based explainable computational framework able to process individual genomic data to make predictions on latent human potentials. As a measure of biological reliability and transparency, the system incorporates scientifically-validated SNP-trait associations in repositories like SNPedia and NHGRI-EBI GWAS Catalog. In contrast to black-box machine learning models, the framework uses deterministic rule logic that explicitly connects all inferred traits with their genetic evidences so that the users can easily map each prediction to its single-nucleotide polymorphism. It is written in Python and Streamlit, a lightweight and privacy-respecting environment to run the engine locally without having to use cloud-based computation. The validity of the system is proven by experimentation, which has shown that the system is capable of working with large genomic datasets and generating interpretable profiles of traits including cognitive, physical and behavioral aspects. The trait generated is not only more informative on the part of the users of genetic predispositions but also creates precedence on ethically and self-aware genomic analysis. The Genomic Insight-Engine combines transparency, efficiency, and privacy to bring about the next era in elucidating bioinformatics concerning personal matters, and lead the way to individualized, evidence-based understanding of human potential in the age of accessible genomics.
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Published
2026-08-25
Pages
1-14
Issue
Vol. 5 No. 3 (2026): IJRTTE - 05 - 03
DOI
10.5281/zenodo.22094639
Citation
Gangula Nithya Sree, Muntha Raju, Yerram Sneha, & Sirisha Veluri. (2026). Genomic Insight-Engine: An Explainable Rule-Based Framework for Profiling Human Potentials from SNP Data. In International Journal of Recent Trends in Technology and Engineering (IJRTTE) (Vol. 5, Issue 3, pp. 1–14). NTL Publisher. https://doi.org/10.5281/zenodo.22094639
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Articles

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