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

Automating High Content Screening wi...

Kraus, Oren Zeev.

Linked to FindBook

Google Book

Amazon

博客來

Automating High Content Screening with Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automating High Content Screening with Deep Learning./
Author:	Kraus, Oren Zeev.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	112 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10688740
ISBN:	9780438186767

Automating High Content Screening with Deep Learning.
Kraus, Oren Zeev.

Automating High Content Screening with Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 112 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2018.

High-content screening (HCS) combines automated microscopy with high throughput technologies for assaying genetic or environmental perturbations. HCS systems have enabled experiments designed to infer relationships between genetic mutations, compound libraries, and cellular phenotypes. Despite the benefits of image-based screens, analyzing large amounts of microscopy data remains a challenging bottleneck. Complex patterns, such as sub-cellular protein localizations, are difficult to quantify and several research groups have resorted to scoring these phenotypes manually by eye. Other researchers have developed customized computational analysis pipelines that need to be re-engineered for every screen. In this thesis, I apply supervised deep learning to HCS datasets as a computational approach that can be used to analyze a wide range of microscopy screens.

ISBN: 9780438186767Subjects--Topical Terms:

553671
Bioinformatics.

Automating High Content Screening with Deep Learning.
LDR:03318nmm a2200349 4500 001 2164619
005 20181116131020.5
008 190424s2018 ||||||||||||||||| ||eng d
020 $a 9780438186767
035 $a (MiAaPQ)AAI10688740
035 $a (MiAaPQ)toronto:17022
035 $a AAI10688740
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kraus, Oren Zeev. $3 3352671
245 1 0 $a Automating High Content Screening with Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 112 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Adviser: Brendan J. Frey.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2018.
520 $a High-content screening (HCS) combines automated microscopy with high throughput technologies for assaying genetic or environmental perturbations. HCS systems have enabled experiments designed to infer relationships between genetic mutations, compound libraries, and cellular phenotypes. Despite the benefits of image-based screens, analyzing large amounts of microscopy data remains a challenging bottleneck. Complex patterns, such as sub-cellular protein localizations, are difficult to quantify and several research groups have resorted to scoring these phenotypes manually by eye. Other researchers have developed customized computational analysis pipelines that need to be re-engineered for every screen. In this thesis, I apply supervised deep learning to HCS datasets as a computational approach that can be used to analyze a wide range of microscopy screens.
520 $a First, I show that the classification performance of a deep convolutional neural network (CNN) outperforms a previously published computational pipeline for assessing the subcellular localization of fluorescently tagged proteins in genome wide yeast screens. I further show that this CNN can be used and updated on other protein localization datasets, including one generated by a different lab and on different imaging equipment. The CNN was used to identify ~300 proteins that change in localization in response to mating pheromone exposure. I trained a similar CNN to assign single cells in HCS images to 1 of 6 cell cycle stage categories, enabling in-silico synchronization of single cells. I combined the outputs from these two CNNs with data from RNA sequencing and ribosome profiling experiments to generate a comprehensive database of protein dynamics throughout the cell cycle.
520 $a Additionally, I developed a novel CNN architecture to train models on HCS images without requiring cell segmentation or annotations for individual cells. This architecture only requires a list of phenotypes evident in a handful of images for training and can then predict which phenotypes are visible in other images and can indicate which cells in the images exhibit them. This model takes a major step toward automating the analysis of HCS images as minimal input is required to use it for analyzing different screens.
590 $a School code: 0779.
650 4 $a Bioinformatics. $3 553671
650 4 $a Computer science. $3 523869
650 4 $a Cellular biology. $3 3172791
690 $a 0715
690 $a 0984
690 $a 0379
710 2 $a University of Toronto (Canada). $b Electrical and Computer Engineering. $3 2096349
773 0 $t Dissertation Abstracts International $g 79-12B(E).
790 $a 0779
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10688740