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Predicting the Lineage Choice of Hematopoietic Stem Cells
Manuel Kroiss
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Description for Predicting the Lineage Choice of Hematopoietic Stem Cells
Paperback. Series: BestMasters. Num Pages: 83 pages, 6 black & white tables, biography. BIC Classification: PNN; PNRD; TDC. Category: (G) General (US: Trade). Dimension: 210 x 148 x 5. Weight in Grams: 126.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer Fachmedien Wiesbaden Germany
Number of pages
83
Condition
New
Series
BestMasters
Number of Pages
68
Place of Publication
Weisbaden, Germany
ISBN
9783658128784
SKU
V9783658128784
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Manuel Kroiss
After finishing his MSc in Bioinformatics, Manuel Kroiss moved to London to work for a computer science company. In his work, the author is focusing on algorithmic problem solving while still remaining interested in applied machine learning.
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