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  • Open Access


    Identification of Circular RNA hsa-circ-0006969 as a Novel Biomarker for Breast Cancer

    Libin Wang1,2,3,#, Xiaohan Li1,3,#, Jinhai Tian1,2,3, Jingjing Yu1,2,3, Qi Huang1,2,3, Rong Ma1,2,3, Jia Wang1,2,3, Jia Cao1,2,3, Jinping Li4,*, Xu Zhang1,2,3,*

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

    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 analysis were applied to… More >

  • Open Access


    Expression and Clinical Significance of ACTA2 in Osteosarcoma Tissue

    Lina Tang1,2, Haiyan Hu2, Yan Zhou2, Yujing Huang2, Yonggang Wang2, Yawen Zhang2, Jinrong Liang2, Zhenxin Wang1,*

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

    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 high-expression group and… More >

  • Open Access


    An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

    G. Anurekha*, P. Geetha

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

    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 classification. To overcome the challenges,… More >

  • Open Access


    Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

    Areej A. Malibari1, Reem M. Alshehri2, Fahd N. Al-Wesabi3, Noha Negm3, Mesfer Al Duhayyim4, Anwer Mustafa Hilal5,*, Ishfaq Yaseen5, Abdelwahed Motwakel5

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

    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 feature selection technique is used… More >

  • Open Access


    Identification of Bio-Markers for Cancer Classification Using Ensemble Approach and Genetic Algorithm

    K. Poongodi1,*, A. Sabari2

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

    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 uses Mahalanobis Distance (MD) as… More >

  • Open Access


    An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

    R. Sowmyalakshmi1,*, Mohamed Ibrahim Waly2, Mohamed Yacin Sikkandar2, T. Jayasankar1, Sayed Sayeed Ahmad3, Rashmi Rani3, Suresh Chavhan4,5

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

    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 compressing microarray images without any… More >

  • Open Access


    A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

    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

    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 tree, and the random forest… More >

  • Open Access


    Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

    Swathypriyadharsini P1,∗, K.Premalatha2,†

    Computer Systems Science and Engineering, Vol.35, No.1, pp. 39-50, 2020, DOI:10.32604/csse.2020.35.039

    Abstract Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding… More >

  • Open Access


    Overview of genetic causes of recurrent miscarriage and the diagnostic approach

    Tarek A ATIA

    BIOCELL, Vol.43, No.4, pp. 253-262, 2019, DOI:10.32604/biocell.2019.08180

    Abstract Recurring miscarriage (RM) is a frustrating reproductive complication with variable etiology. Numerous genetic defects have been known to play a crucial role in the etiology of RM. Chromosomal abnormalities are frequently detected, while other genetic defects cannot be diagnosed through routine research, such as cryptic chromosomal anomalies, single nucleotide polymorphism, single-gene defect, and gene copy number variation. Diagnostic laboratories have recently used variable advanced techniques to detect potential genetic abnormalities in couples with RM and/or in products of conception. Here we aim to summarize the known genetic causes of RM, with a focus on the new diagnostic techniques. Knowledge of… More >

  • Open Access


    Gene expression profiling of HepG2 cells after treatment with black tea polyphenols

    Jie Zhong1,#, Li Deng2,#, Yu Jiang3, Lianhong Zou3, Huabing Yuan4, Shuang-xiang Tan1,*

    BIOCELL, Vol.42, No.3, pp. 99-104, 2018, DOI:10.32604/biocell.2018.04915

    Abstract This study aimed to determine the effects of black tea polyphenols on gene expression in hepatocellular cancer cells. The total RNA from HepG2 hepatocellular cancer cells treated with black tea polyphenols was subjected to Human 14K cDNA microarray analysis. Real-time PCR and Western blot analysis were conducted to verify microarray data. Black tea polyphenols treatment at the dose of 20 mg/L, 40 mg/L or 80 mg/L for one to three days inhibited the growth of HepG2 cells in a dose and time dependent manner. A total of 48 genes showed more than two-fold change after black tea polyphenols treatment, including… More >

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