Segmenting Microarrays with Deep Neural Networks

Jones, AL

doi:10.1101/020404

Cited by 2 publications

(2 citation statements)

References 17 publications

(14 reference statements)

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“…In one application of deep learning techniques to this problem, a CNN was used for microarray image segmentation and demonstrated results in accuracy that resembled baseline approaches in accuracy, but with easier training and fewer requirements of computational sources. 29 Another opportunity for the application of CNNs to imagebased gene expression data has been RNA in situ hybridization, a tedious technique that enables localization and visualization of gene expression in a group of cells, tissue slice, or whole organism when such manipulations are allowed. This method facilitates powerful longitudinal studies that illustrate changes in expression patterns during development.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…In microrarray analysis, detection of a signal and recognition of fluorescence spots can be challenging because of variation in spot size, shape, location, or signal intensity, and fluorescence signal intensity often corresponds poorly to gene or sequence expression level. In one application of deep learning techniques to this problem, a CNN was used for microarray image segmentation and demonstrated results in accuracy that resembled baseline approaches in accuracy, but with easier training and fewer requirements of computational sources …”

Section: Deep Learning Studies and Potential Applications In Biomedicinementioning

confidence: 99%

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Applications of Deep Learning in Biomedicine

Vieira²,

et al. 2016

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Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Section: Deep Learning Studies and Potential Applications In Biomedicinementioning

confidence: 99%

Applications of Deep Learning in Biomedicine

Vieira²,

et al. 2016

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Biological image analysis using deep learning-based methods: Literature review

et al. 2018

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Automatic processing large amount of microscopic images is important for medical and biological studies. Deep learning has demonstrated better performance than traditional machine learning methods for processing massive quantities of images; therefore, it has attracted increasing attention from the research and industry fields. This paper summarizes the latest progress of deep learning methods in biological microscopic image processing, including image classification, object detection, and image segmentation. Compared to the traditional machine learning methods, deep neural networks achieved better accuracy without tedious feature selection procedure. Obstacles of the biological image analysis with deep learning methods include limited training set and imperfect image quality. Viable solutions to these obstacles are discussed at the end of the paper. With this survey, we hope to provide a reference for the researchers conducting biological microscopic image processing.

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Segmenting Microarrays with Deep Neural Networks

Cited by 2 publications

References 17 publications

Applications of Deep Learning in Biomedicine

Applications of Deep Learning in Biomedicine

Biological image analysis using deep learning-based methods: Literature review

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