An Automated cDNA Microarray Image Analysis for the Determination of Gene Expression Ratios

Joseph, Steffy Maria; Sathidevi, P. S.

doi:10.1109/tcbb.2021.3135650

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Notably, the log-R ratio demonstrates a correlation with gene expression levels [69]. Recent research determined gene expression levels by analyzing microarray images using log ratio [70,71].…”

Section: Discussionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

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Section: Discussionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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cDNA Microarray Image Segmentation Based on Deep Convolutional Neural Network

Shao,

Cheng,

Zhu

et al. 2023

2023 13th International Conference on Information Technology in Medicine and Education (ITME)

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Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches

Baloi

Costea

Gutt

et al. 2023

Sensors

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Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones.

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An Automated cDNA Microarray Image Analysis for the Determination of Gene Expression Ratios

Cited by 3 publications

References 47 publications

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

cDNA Microarray Image Segmentation Based on Deep Convolutional Neural Network

Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches

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