Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks
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.
1123262694
Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks
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.
54.99 Out Of Stock
Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks

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

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

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

by Manuel Kroiss

Overview

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.

Product Details

ISBN-13: 9783658128784
Publisher: Springer Fachmedien Wiesbaden
Publication date: 05/13/2016
Series: BestMasters
Edition description: 1st ed. 2016
Pages: 68
Product dimensions: 5.83(w) x 8.27(h) x (d)

About the Author

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.

Table of Contents

Machine Learning – Deep Learning.- Training Neural Networks.- Recurrent Neural Networks.- Stem Cell Classification Using Microscopy Images.

From the B&N Reads Blog
Page 1 of

Related Subjects

Science & Technology
Chemistry
Organic Chemistry
Science & Technology
Chemistry
Organic Chemistry

Customer Reviews