Book Description Soft Cover. Condition: new. Seller Inventory # 9789811630637
Book Description Condition: New. Seller Inventory # ABLIING23Apr0412070092131
Book Description Condition: New. Seller Inventory # 43617561-n
Book Description Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9789811630637_lsuk
Book Description Paperback. Condition: Brand New. 122 pages. 9.25x6.10x0.26 inches. In Stock. Seller Inventory # x-9811630631
Book Description Condition: New. Seller Inventory # 43617561-n
Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning. 124 pp. Englisch. Seller Inventory # 9789811630637
Book Description Condition: New. 2021. 1st ed. 2021. paperback. . . . . . Seller Inventory # V9789811630637
Book Description Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The emerging biotechnologies have significantly advanced the study of biological mechanisms. However, biological data usually contain a great amount of missing information, e.g. missing features, missing labels or missing samples, which greatly limits the extensive usage of the data. In this book, we introduce different types of biological data missing scenarios and propose machine learning models to improve the data analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and structure-aware rebalancing for minor sample missings. Models in the book cover the fields of imbalance learning, deep learning, recurrent neural network and statistical inference, providing a wide range of references of the integration between artificial intelligence and biology. With simulated and biological datasets, we apply approaches to a variety of biological tasks, including single-cell characterization, genome-wide association studies, medical image segmentations, and quantify the performances in a number of successful metrics.The outline of this book is as follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery of missing labels; in Chapter 4, we introduce the statistical recovery of missing data sample information; finally, in Chapter 5, we summarize the full text and outlook future directions. This book can be used as references for researchers in computational biology, bioinformatics and biostatistics. Readers are expected to have basic knowledge of statistics and machine learning. Seller Inventory # 9789811630637
Book Description Condition: New. 2021. 1st ed. 2021. paperback. . . . . . Books ship from the US and Ireland. Seller Inventory # V9789811630637