東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Predicting the lineage choice of hem...

Kroiss, Manuel.

Linked to FindBook

Google Book

Amazon

博客來

Predicting the lineage choice of hematopoietic stem cells = a novel approach using deep neural networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting the lineage choice of hematopoietic stem cells/ by Manuel Kroiss.
Reminder of title:	a novel approach using deep neural networks /
Author:	Kroiss, Manuel.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2016.,
Description:	xv, 68 p. :ill., digital ;24 cm.
[NT 15003449]:	Machine Learning - Deep Learning -- Training Neural Networks -- Recurrent Neural Networks -- Stem Cell Classification Using Microscopy Images.
Contained By:	Springer eBooks
Subject:	Hematopoietic stem cells. -
Online resource:	http://dx.doi.org/10.1007/978-3-658-12879-1
ISBN:	9783658128791

Predicting the lineage choice of hematopoietic stem cells = a novel approach using deep neural networks /
Kroiss, Manuel.

Predicting the lineage choice of hematopoietic stem cellsa novel approach using deep neural networks /[electronic resource] :by Manuel Kroiss. - Wiesbaden :Springer Fachmedien Wiesbaden :2016. - xv, 68 p. :ill., digital ;24 cm. - BestMasters. - BestMasters..

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

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. Contents Machine Learning - Deep Learning Training Neural Networks Recurrent Neural Networks Stem Cell Classification Using Microscopy Images Target Groups Teachers and students in the field of computer science and applied machine learning Executives and specialists in the field of neural networks and computational biology 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.

ISBN: 9783658128791

Standard No.: 10.1007/978-3-658-12879-1doiSubjects--Topical Terms:

600616
Hematopoietic stem cells.

LC Class. No.: QH588.S83

Dewey Class. No.: 616.02774

Predicting the lineage choice of hematopoietic stem cells = a novel approach using deep neural networks /
LDR:02665nmm a2200325 a 4500 001 2037932
003 DE-He213
005 20161020153102.0
006 m d
007 cr nn 008maaau
008 161209s2016 gw s 0 eng d
020 $a 9783658128791 $q (electronic bk.)
020 $a 9783658128784 $q (paper)
024 7 $a 10.1007/978-3-658-12879-1 $2 doi
035 $a 978-3-658-12879-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QH588.S83
072 7 $a PNN $2 bicssc
072 7 $a SCI013040 $2 bisacsh
082 0 4 $a 616.02774 $2 23
090 $a QH588.S83 $b K93 2016
100 1 $a Kroiss, Manuel. $3 2194794
245 1 0 $a Predicting the lineage choice of hematopoietic stem cells $h [electronic resource] : $b a novel approach using deep neural networks / $c by Manuel Kroiss.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Spektrum, $c 2016.
300 $a xv, 68 p. : $b ill., digital ; $c 24 cm.
490 1 $a BestMasters
505 0 $a Machine Learning - Deep Learning -- Training Neural Networks -- Recurrent Neural Networks -- Stem Cell Classification Using Microscopy Images.
520 $a 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. Contents Machine Learning - Deep Learning Training Neural Networks Recurrent Neural Networks Stem Cell Classification Using Microscopy Images Target Groups Teachers and students in the field of computer science and applied machine learning Executives and specialists in the field of neural networks and computational biology 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.
650 0 $a Hematopoietic stem cells. $3 600616
650 1 4 $a Chemistry. $3 516420
650 2 4 $a Organic Chemistry. $3 890856
650 2 4 $a Catalysis. $3 560465
650 2 4 $a Industrial Chemistry/Chemical Engineering. $3 890826
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 2056364
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-12879-1
950 $a Chemistry and Materials Science (Springer-11644)