| 書目名稱 | Computational Reconstruction of Missing Data in Biological Research | | 編輯 | Feng Bao | | 視頻video | http://file.papertrans.cn/233/232935/232935.mp4 | | 叢書名稱 | Springer Theses | | 圖書封面 |  | | 描述 | 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 | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | Machine Learning; Biological data analysis; Data imputation; Imbalance learning; Single-cell analysis | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-16-3064-4 | | isbn_softcover | 978-981-16-3063-7 | | isbn_ebook | 978-981-16-3064-4Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | Tsinghua University Press 2021 |
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