| 書目名稱 | Predicting the Lineage Choice of Hematopoietic Stem Cells |
| 副標(biāo)題 | A Novel Approach Usi |
| 編輯 | Manuel Kroiss |
| 視頻video | http://file.papertrans.cn/755/754544/754544.mp4 |
| 概述 | Publication in the Field of Organic Chemistry |
| 叢書名稱 | BestMasters |
| 圖書封面 |  |
| 描述 | Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines. |
| 出版日期 | Book 2016 |
| 關(guān)鍵詞 | hematopoietic stem cells; machine learning; deep neural networks; recurrent Neural Networks; predict lin |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-658-12879-1 |
| isbn_softcover | 978-3-658-12878-4 |
| isbn_ebook | 978-3-658-12879-1Series ISSN 2625-3577 Series E-ISSN 2625-3615 |
| issn_series | 2625-3577 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies |
|Archiver|手機(jī)版|小黑屋|
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