Mutasem K. Alsmadi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065 - 20 June 2024

Abstract Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally,… More >

Hala AlShamlan^*, Halah AlMazrua^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146 - 25 April 2024

Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine… More >

Abdel Nasser H. Zaied¹, Ehab Rushdy², Mona Gamal^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596 - 28 July 2023

Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to… More >

Aliaa El-Gawady^1,*, BenBella S. Tawfik¹, Mohamed A. Makhlouf^1,2

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5559-5572, 2023, DOI:10.32604/cmc.2023.034734 - 28 December 2022

Abstract Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are… More >

G. Anurekha^*, P. Geetha

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3049-3064, 2023, DOI:10.32604/iasc.2023.029127 - 17 August 2022

Abstract Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and… More >

Heyam H. Al-Baity^*, Nourah Al-Mutlaq

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291 - 01 March 2021

Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision… More >

Yinjie Wang, Yongxia Zhang, Qingquan Liu, Liangqin Liu, Suzhen Huang, Haiyan Yuan^*

Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 277-290, 2021, DOI:10.32604/phyton.2020.011545 - 20 November 2020

Abstract Quantitative real-time PCR (qPCR) is an effective and widely used method to analyze expression patterns of target genes. Selection of stable reference genes is a prerequisite for accurate normalization of target gene expression by qRT-PCR. In Iris germanica L., no studies have yet been published regarding the evaluation of potential reference genes. In this study, nine candidate reference genes were assessed at different flower developmental stages and in different tissues by four different algorithms (GeNorm, NormFinder, BestKeeper, and RefFinder). The results revealed that ACT11 (Actin 11) and EF1α (Elongation factor 1 alpha) were the most stable reference… More >

Lu Zhu, Qiuyue Ma, Shushun Li, Jing Wen, Kunyuan Yan, Qianzhong Li^*

Phyton-International Journal of Experimental Botany, Vol.89, No.2, pp. 385-403, 2020, DOI:10.32604/phyton.2020.09259 - 22 April 2020

Abstract Quantitative real-time reverse transcriptase PCR (qRT-PCR) technology has been extensively used to estimate gene expression levels, and the selection of appropriate reference genes for qRT-PCR analysis is critically important for obtaining authentic normalized data. Acer palmatum is an important colorful leaf ornamental tree species, and reference genes suitable for normalization of the qRT-PCR data obtained from this species have not been investigated. In this study, the expression stability of ten candidate reference genes, namely, Actin3, Actin6, Actin9, EF1α, PP2A, SAMDC, TIP41, TUBα, TUBβ and UBQ10, in two distinct tissues (leaves and roots) of A. palmatum under four different abiotic stress conditions (cold,… More >

Gang Li¹, Yao Zhou², Yaqi Zhao², Yaxue Liu², Yuwei Ke², Xiaoqing Jin¹, Haoli Ma^1,2,*

Phyton-International Journal of Experimental Botany, Vol.89, No.2, pp. 329-344, 2020, DOI:10.32604/phyton.2020.08874 - 22 April 2020

Abstract Quantitative real-time PCR (qRT-PCR) is widely used for investigating gene expression patterns and has many advantages, including its high sensitivity, fidelity, and specificity. Selecting a satisfactory internal reference gene is crucial for obtaining precise gene expression results in qRT-PCR analyses. In this study, the transcriptomic data of 2 potato varieties were screened for housekeeping genes with stable expression patterns. A total of 77 putative genes were selected, which were highly and stably expressed. Then, qRT-PCR analyses were performed to examine the expression levels of these 77 candidate reference genes in various potato tissues, including leaves, More >