Data Acquisition And Preprocessing In Bioinformatics(1st Edition)

Book details

Book Price $0 : Are you fascinated by the intersection of bioinformatics and data science? Do you want to master the techniques used in acquiring and preprocessing biological data? Look no further! This comprehensive guide, with Python code examples for each chapter, delves into the world of data acquisition and preprocessing in bioinformatics.Key Features:- Explore a wide range of topics, from probabilistic graphical models to quantum machine learning applications- Gain hands-on experience with Python code in each chapter- Learn how to handle large biological datasets and apply advanced machine learning techniques- Understand ethical considerations and algorithmic biases in bioinformatics- Discover how to leverage pre-trained models and use self-supervised learning in genomic datasets- Apply recommender systems and matrix factorization techniques to extract patterns from biological matricesBook Description:"Data Acquisition and Preprocessing in Bioinformatics" unravels the complexities of exploring biological data using cutting-edge data acquisition and preprocessing techniques in bioinformatics. With 33 comprehensive chapters, this book covers a wide range of topics crucial in the field, including probabilistic graphical models, transfer learning, attention mechanisms, adversarial learning, and experimental design informed by machine learning models.Throughout this book, readers will gain a deeper understanding of how these techniques can be applied to real-world biological problems. Python code is provided in each chapter, allowing readers to follow along and implement these strategies in their own projects. By the end of this book, readers will have the skills and knowledge necessary to confidently acquire and preprocess biological data.What you will learn:- Understand the fundamentals of probabilistic graphical models, Monte Carlo simulations, and Markov Chain Monte Carlo- Explore advanced techniques such as Gibbs Sampling, Latent Dirichlet Allocation, and Variational Inference- Apply sparse regression techniques, feature engineering, and kernel methods to simplify biological data- Gain insights into ensemble learning techniques, dimensionality reduction with ICA, and gradient boosting machines- Harness the power of Bayesian optimization and adversarial learning in simulating biological phenomena- Utilize attention mechanisms, transfer learning, and self-supervised learning in bioinformatics- Address algorithmic biases, ethical considerations, and privacy concerns in working with biological data- Apply recommender systems, matrix factorization techniques, and spectral clustering in biological contextsWho this book is for:This book is essential for data scientists, bioinformaticians, researchers, and students interested in expanding their knowledge of data acquisition and preprocessing in the field of bioinformatics. Basic familiarity with Python programming is assumed, but no background in bioinformatics is required. Whether you are a beginner or an experienced practitioner, this book will take your skills to the next level and help you excel in analyzing biological data. See more