Effective Gene Expression Annotation Approaches for Mouse Brain Images

abstract: Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. Th...

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Other Authors: Zhao, Xinlin (Author)
Format: Dissertation
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.36531
id ndltd-asu.edu-item-36531
record_format oai_dc
spelling ndltd-asu.edu-item-365312018-06-22T03:06:57Z Effective Gene Expression Annotation Approaches for Mouse Brain Images abstract: Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy. Dissertation/Thesis Zhao, Xinlin (Author) Ye, Jieping (Advisor) Wang, Yalin (Advisor) Li, Baoxin (Committee member) Arizona State University (Publisher) Computer science Gene Expression Image Annotation Multi-label Sparse Coding eng 57 pages Masters Thesis Computer Science 2016 Masters Thesis http://hdl.handle.net/2286/R.I.36531 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2016
collection NDLTD
language English
format Dissertation
sources NDLTD
topic Computer science
Gene Expression
Image Annotation
Multi-label
Sparse Coding
spellingShingle Computer science
Gene Expression
Image Annotation
Multi-label
Sparse Coding
Effective Gene Expression Annotation Approaches for Mouse Brain Images
description abstract: Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy. === Dissertation/Thesis === Masters Thesis Computer Science 2016
author2 Zhao, Xinlin (Author)
author_facet Zhao, Xinlin (Author)
title Effective Gene Expression Annotation Approaches for Mouse Brain Images
title_short Effective Gene Expression Annotation Approaches for Mouse Brain Images
title_full Effective Gene Expression Annotation Approaches for Mouse Brain Images
title_fullStr Effective Gene Expression Annotation Approaches for Mouse Brain Images
title_full_unstemmed Effective Gene Expression Annotation Approaches for Mouse Brain Images
title_sort effective gene expression annotation approaches for mouse brain images
publishDate 2016
url http://hdl.handle.net/2286/R.I.36531
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