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Predicting the Lineage Choice of Hematopoietic Stem Cells - A Novel Approach Using Deep Neural Networks (Paperback, 1st ed. 2016)
Loot Price: R1,676
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Predicting the Lineage Choice of Hematopoietic Stem Cells - A Novel Approach Using Deep Neural Networks (Paperback, 1st ed. 2016)
Series: BestMasters
Expected to ship within 10 - 15 working days
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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.
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