Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

YAO FANG^1,2,#, FEI XIA^1,#, FEIFEI TIAN³, LEI QU¹, FANG YANG¹, JUAN FANG^1,2, ZHENHONG HU^1,*, HAICHAO LIU^1,*

BIOCELL, Vol.48, No.4, pp. 613-621, 2024, DOI:10.32604/biocell.2024.031539 - 09 April 2024

Abstract Objective: In the realm of Class I pathogens, Burkholderia pseudomallei (BP) stands out for its propensity to induce severe pathogenicity. Investigating the intricate interactions between BP and host cells is imperative for comprehending the dynamics of BP infection and discerning biomarkers indicative of the host cell response process. Methods: mRNA extraction from BP-infected mouse macrophages constituted the initial step of our study. Employing gene expression arrays, the extracted RNA underwent conversion into digital signals. The percentile shift method facilitated data processing, with the identification of genes manifesting significant differences accomplished through the application of the t-test. Subsequently,… 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 >

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 >

Libin Wang^1,2,3,#, Xiaohan Li^1,3,#, Jinhai Tian^1,2,3, Jingjing Yu^1,2,3, Qi Huang^1,2,3, Rong Ma^1,2,3, Jia Wang^1,2,3, Jia Cao^1,2,3, Jinping Li^4,*, Xu Zhang^1,2,3,*

Oncologie, Vol.24, No.4, pp. 789-801, 2022, DOI:10.32604/oncologie.2022.026589 - 31 December 2022

Abstract Background: To investigate the characteristics of circular RNA hsa-circ-0006969 in breast cancer and identify it as a novel biomarker for breast cancer. Methods: Three breast cancer (BC) patient tissues were selected to perform human circRNA microarray analysis. GeneSpring 13.0 (Agilent) software was applied for analyzing the data. Another 116 BC patients were recruited for verification. Hsa-circ-0006969 was found as a potential circRNA for BC diagnostic biomarker. The structure of hsa-circ-0006969 was predicted by circPrimer1.2 software. MiRanda v3.3, RNA hybrid 2.1, and Cytoscape 3.6.0 were used for predicting the networks of circRNA-miRNA. T-test, Curve regression, and ROC… More >

Lina Tang^1,2, Haiyan Hu², Yan Zhou², Yujing Huang², Yonggang Wang², Yawen Zhang², Jinrong Liang², Zhenxin Wang^1,*

Oncologie, Vol.24, No.4, pp. 913-925, 2022, DOI:10.32604/oncologie.2022.026296 - 31 December 2022

Abstract Objective: To investigate the expression of alpha–smooth muscle actin (ACTA2) in osteosarcoma tissues and its relationship with prognosis. Methods: Prognostic analysis of lung metastasis–related genes in osteosarcoma using the TCGA database. Single-cell sequencing detected the expression of ACTA2 in 11 osteosarcoma tissues. Paraf- fin-embedded tissues of 74 osteosarcoma patients treated at the Sixth People’s Hospital of Shanghai Jiao Tong University from 2014 to 2019 were collected, and tissue microarrays were prepared. ACTA2 expression was detected and scored by immunohistochemistry. According to the median value of the ACTA2 histochemical score, 74 patients were divided into two groups, the… More >

Areej A. Malibari¹, Reem M. Alshehri², Fahd N. Al-Wesabi³, Noha Negm³, Mesfer Al Duhayyim⁴, Anwer Mustafa Hilal^5,*, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4277-4290, 2022, DOI:10.32604/cmc.2022.027030 - 16 June 2022

Abstract In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based… More >

K. Poongodi^1,*, A. Sabari²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 939-953, 2022, DOI:10.32604/iasc.2022.023038 - 08 February 2022

Abstract The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which More >

R. Sowmyalakshmi^1,*, Mohamed Ibrahim Waly², Mohamed Yacin Sikkandar², T. Jayasankar¹, Sayed Sayeed Ahmad³, Rashmi Rani³, Suresh Chavhan^4,5

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2245-2260, 2021, DOI:10.32604/cmc.2021.018636 - 21 July 2021

Abstract In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images. It remains a major challenge to process, store and transmit such huge volumes of microarray images. So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily. Various techniques have been proposed in the past with applications in different domains. The current research paper presents a novel image compression technique i.e., optimized Linde–Buzo–Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for… 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 >