Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation

Belean, Bogdan; Gutt, Robert; Costea, Carmen; Bălăcescu, Ovidiu

doi:10.1109/access.2020.3019844

Cited by 12 publications

(6 citation statements)

References 34 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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“…Bogdan Belean [1] discussed about the fact, microarray experiments can be used to examine the phenotypic, pathology, functionality, and reaction of cells to a drug therapy. It results in the simultaneous characterization of the levels of gene expression for all cellular transcripts in a single experiment.…”

Section: Literature Surveymentioning

confidence: 99%

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

2023

IRJMETS

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A potent technique for examining the gene expression patterns of various organisms is microarray technology. Unfortunately, microarray data analysis is frequently time-consuming and computationally intensive, particularly when working with large datasets. Using parallel computing techniques, we suggest a quick computation method for microarray data analysis in this study. Using a combination of the Expectation-Maximization (EM) algorithm, 3D printing, the Generalized Hidden Markov Model (GHMM) framework, and Lab-on-a-Chip (LOC) devices, the study suggests a unique method for the quick calculation of microarray data. The suggested method attempts to speed up microarray analysis computations and improve its precision, which is important for discovering gene expression patterns and biomarkers linked to various disorders. The LOC devices for sample preparation and analysis are made using a 3D printer, the EM method is utilized to estimate the model parameters, and the GHMM framework is used to model the gene expression patterns. The proposed method has demonstrated promising accuracy and computational results, making it a suitable remedy for microarray data analysis in clinical research. The processing of microarray data is distributed using the MapReduce architecture in the method, which speeds up computing. We also employ a speedy technique for data standardization and filtering, which speeds up the processing even more. It shows how quickly our technology can handle big microarray datasets while still producing findings with a high degree of precision and accuracy.

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“…Satyasis et al [12] proposed an improved fast and robust FCM algorithm (IFRFCM) to improve the noise reduction capability. Belean et al [14] proposed a density-based spatial clustering procedure driven by a level-set approach for microarray spot segmentation and quality measures were obtained. Wenxiu et al [15] proposed a two-phase selective segmentation method, in which the first phase reduces noise on segmentation and the second phase shows the selective segmentation on the preprocessed image.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model

Sahoo¹,

Mishra²,

Mohanty³

2022

IJACSA

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Image segmentation is an essential technique of brain tumor MRI image processing for automated diagnosis of an image by partitioning it into distinct regions referred to as a set of pixels. The classification of the tumor affected and nontumor becomes an arduous task for radiologists. This paper presents a novel image enhancement based on the SCA (Sine Cosine Algorithm) optimization technique for the improvement of image quality. The improved FLICM (Fuzzy Local Information C Means) segmentation technique is proposed to detect the affected regions of brain tumor from the MRI brain tumor images and reduction of noise from the MRI images by introducing a fuzzy factor to the objective function. The SCA weight-optimized Wavelet-Extreme Learning Machine (SCA-WELM) model is also proposed for the classification of benign tumors and malignant tumors from MRI brain images. In the first instance, the enhanced images are undergone improved FLICM Segmentation. In the second phase, the segmented images are utilized for feature extraction. The GLCM feature extraction technique is considered for feature extraction. The extracted features are aligned as input to the SCA-WELM model for the classification of benign and malignant tumors. The following dataset (Dataset-255) is considered for evaluating the proposed classification approach. An accuracy of 99.12% is achieved by the improved FLICM segmentation technique. The classification performance of the SCA-WELM is measured by sensitivity, specificity, accuracy, and computational time and achieved 0.98, 0.99, 99.21%, and 97.2576 seconds respectively. The comparison results of SVM (Support Vector Machine), ELM, SCA-ELM, and proposed SCA-WELM models are presented to show the robustness of the proposed SCA-WELM classification model.

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Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation

Cited by 12 publications

References 34 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

Survey on Accelerating Biomedical Research With Fast Computation Methods for Microarray Data Analysis

Brain Tumor Segmentation and Classification from MRI Images using Improved FLICM Segmentation and SCA Weight Optimized Wavelet-ELM Model

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