Unlocking Biomarker Identification – Harnessing AI and ML for Precision Medicine

Description

computational techniques to analyze genetic data for identifying biomarkers. These biomarkers are crucial for diagnosing diseases, predicting outcomes, and personalizing treatments. The book covers various machine learning algorithms, such as deep learning, support vector machines, and random forests, explaining how they can be applied to genomic datasets. It discusses feature selection methods, data pre-processing, and the challenges of dealing with high-dimensional data. Case studies and real-world applications illustrate the practical aspects. Additionally, the book addresses ethical considerations and data privacy issues. It is an invaluable resource for bioinformaticians, computational biologists, and healthcare professionals seeking to harness machine learning for genomic.