國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Predicting the lineage choice of hematopoietic stem cells/ by Manuel Kroiss.
其他題名:	a novel approach using deep neural networks /
作者:	Kroiss, Manuel.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2016.,
面頁冊數:	xv, 68 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Hematopoietic stem cells. -
電子資源:	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:

857347
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:02665nam a2200325 a 4500 001 864112
003 DE-He213
005 20161020153102.0
006 m d
007 cr nn 008maaau
008 170720s2016 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 1108739
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 857347
650 1 4 $a Chemistry. $3 593913
650 2 4 $a Organic Chemistry. $3 673440
650 2 4 $a Catalysis. $3 673438
650 2 4 $a Industrial Chemistry/Chemical Engineering. $3 671153
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 1021672
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-12879-1
950 $a Chemistry and Materials Science (Springer-11644)