Predicting the Lineage Choice of Hematopoietic Stem Cells A Nove
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 pres
- PDF / 2,033,296 Bytes
- 80 Pages / 419.58 x 595.28 pts Page_size
- 17 Downloads / 154 Views
Predicting the Lineage Choice of Hematopoietic Stem Cells A Novel Approach Using Deep Neural Networks
BestMasters
Weitere Informationen zu dieser Reihe finden Sie unter http://www.springer.com/series/13198
Springer awards „BestMasters“ to the best master’s theses which have been completed at renowned universities in Germany, Austria, and Switzerland. The studies received highest marks and were recommended for publication by supervisors. They address current issues from various fields of research in natural sciences, psychology, technology, and economics. The series addresses practitioners as well as scientists and, in particular, offers guidance for early stage researchers.
Manuel Kroiss
Predicting the Lineage Choice of Hematopoietic Stem Cells A Novel Approach Using Deep Neural Networks
Manuel Kroiss Neuherberg, Deutschland
BestMasters ISBN 978-3-658-12878-4 ISBN 978-3-658-12879-1 (eBook) DOI 10.1007/978-3-658-12879-1 Library of Congress Control Number: 2016930594 © Springer Fachmedien Wiesbaden 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer Spektrum imprint is published by Springer Nature The registered company is Springer Fachmedien Wiesbaden GmbH
Abstract
V
Abstract We study 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. Identifying the point in time when the decision of the linage choice is made is very difficult. As of now, the two biological markers GATA and FCgamma are used to identify these cells in an invitro experiment and then take them out for further expression analysis. However, prior results showed that an earlier detection might be possible and that the lineage choice seems to be several generations ahead of when the biological markers can identify the cells. We present a novel approach to distinguish MEP from GMP using machine le
Data Loading...
