International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning–Driven Analysis for Disease Gene Detection |
|---|---|
| ABSTRACT | Identifying genes associated with human diseases is vital to advance precision medicine together with understand disease mechanisms. Computational methods have risen because experimental approaches for disease-gene association are often time-consuming and costly. This rise happened as a result of those costly approaches. This study presents such an integrated framework that combines biological sequence information with protein then protein interaction (PPI) networks along with machine learning algorithms in order to identify as well as prioritize disease-associated genes. We extract both biological and topological features by the utilization of datasets such as DisGeNET, GEO, HPRD, and CORUM, and all of them are available publicly. We assess some math models through validation. Results indicate that prediction accuracy improves when multiple feature types are integrated versus single-feature methods. Our approach highlights key genes and pathways since they involve diseases such as Diabetes, Asthma, Thalassemia, and Malaria, which offers a scalable strategy that prioritizes genes and discovers novel targets in biomedical research. |
| AUTHOR | DR. SRINIVASA A H, RAKSHITHA R, NAVYASHREE, RUCHITHA K R Associate Professor, Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India Students, Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312048 |
| pdf/48_Machine Learning–Driven Analysis for Disease Gene Detection.pdf | |
| KEYWORDS | |
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