Predicting the Lineage Choice of Hematopoietic Stem Cells – A Novel Approach Using Deep Neural Networks by Manuel Kroiss

Title: Predicting the Lineage Choice of Hematopoietic Stem Cells – A Novel Approach Using Deep Neural Networks
Editor: Manuel Kroiss
Edition: Illustrated
Publisher: Springer Publications
Length: 80 pages
Size: 1.93 MB
Language: English
Book Description:

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.

Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks

Author(s): Manuel Kroiss (auth.)

Series: BestMasters

Publisher: Springer Spektrum, Year: 2016

ISBN: 978-3-658-12878-4

[PDF] Predicting the Lineage Choice of Hematopoietic Stem Cells Table Of Contents

Front Matter….Pages I-XV
Introduction….Pages 1-7
Introduction to deep neural networks….Pages 9-29
Using RNNs to predict the lineage choice of stem cells….Pages 31-53
Discussion….Pages 55-60
Back Matter….Pages 61-68


Download


Buy From Amazon

Thanks For Visiting Our Website http://www.freepdfbook.com To Support Us, Keep Share On Social Media.