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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21574
Article Published : 8528
Total Authors : 22430
Total Reviewer : 805
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| Paper Title: | AI-driven Analysis Of Drug Side Effects using DNA Profiles |
| Authors Name: | Prasad Alai , Sanket Gadekar , Pranjal Vedpathak , Vighnesh Narawade |
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IJRTI_207422
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| Published Paper Id: | IJRTI2511054 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | Adverse drug reactions (ADRs) are a big cause of hospital visits and preventable health problems around the world. The way drugs are usually prescribed, which is the same for everyone, doesn't take into account how genes can make people react differently to medicines. Pharmacogenomics uses genetic information along with knowledge about how drugs work to create personalized treatment plans. As more genetic data and information about how drugs work becomes available, artificial intelligence (AI) is helping to understand how genetic differences, drug structures, and side effects are connected. This paper looks at recent uses of machine learning and deep learning to predict ADRs, including methods like Random Forest, XGBoost, convolutional neural networks, graph neural networks, and multimodal fusion systems. Research shows that using both genetic data and details about the drug's molecular structure makes predictions better than using just information about the drug alone. Even with these advances, there are still challenges like not enough labeled data, difficulty in understanding how these models work, and problems in using them across different groups of people. The paper also suggests a new AI-based system that connects a patient's genetic information with databases about drug genetics to better predict side effects. This review highlights how combining genetic personalization with powerful AI models can lead to safer and more accurate medical care. |
| Keywords: | Adverse Drug Reactions (ADRs), Pharmacogenomics, Artificial Intelligence (AI), Machine Learning, Deep Learning, Personalized Medicine. |
| Cite Article: | "AI-driven Analysis Of Drug Side Effects using DNA Profiles", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a465-a468, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511054.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511054
Registration ID:207422
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a465-a468 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511054 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511054 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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