October 2024
Intermediate to advanced
560 pages
14h 37m
English
book
Genomics at the Nexus of AI, Computer Vision, and Machine Learning
by Shilpa Choudhary, Sandeep Kumar, Swathi Gowroju, Monali Gulhane, R. Sri Lakshmi
Content preview from Genomics at the Nexus of AI, Computer Vision, and Machine Learning
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7Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey
V. Lakshmi Chetana1* and Hari Seetha2
1Department of Computer Science and Engineering at School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, India
2Center of Excellence, AI and Robotics, VIT-AP University, Near Vijayawada, India
Abstract
Genomics and personalized medicine have revolutionized healthcare by allowing doctors to customize treatments to individual genetic profiles. Genomic recommender systems utilize advanced machine learning techniques to analyze genomic data and provide personalized recommendations for various applications in healthcare and genetics. These systems make recommendations and explain why specific treatments or medications are suggested, giving clarity to patients and building trust in the recommendation. Achieving transparency and interpretability in these systems is crucial to ensure that patients have confidence in their decisions based on their genetic data. This chapter comprehensively reviews existing research in this field, discusses its challenges, and highlights future directions for advancements in explainable genomic recommendation systems.
Keywords: Clinical recommender systems, explainability, explainable genomics, genomic recommender systems, genomic data, healthcare applications, personalized medicine
7.1 Introduction
Genomics studies the entire set of genes, their functions, and their interactions within a living organism. It provides valuable ...
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