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  • Open Access


    Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

    Aliaa El-Gawady1,*, BenBella S. Tawfik1, Mohamed A. Makhlouf1,2

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

    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 preprocessed identically. In the GS… More >

  • Open Access


    Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

    Muhammad Hamraz1, Dost Muhammad Khan1, Naz Gul1, Amjad Ali1, Zardad Khan1, Shafiq Ahmad2, Mejdal Alqahtani2, Akber Abid Gardezi3, Muhammad Shafiq4,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3663-3677, 2023, DOI:10.32604/cmc.2023.030064

    Abstract The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the “Median” as well as the measures of variation i.e., “Median absolute deviation (MAD) and Interquartile range (IQR)” in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR). The results obtained via… 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


    A Novel Soft Clustering Approach for Gene Expression Data

    E. Kavitha1,*, R. Tamilarasan2, Arunadevi Baladhandapani3, M. K. Jayanthi Kannan4

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 871-886, 2022, DOI:10.32604/csse.2022.021215

    Abstract Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to identify the patterns present in… More >

  • Open Access


    Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

    S. Jacophine Susmi*

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1139-1152, 2022, DOI:10.32604/iasc.2022.023865

    Abstract The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections… 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


    Clustering Gene Expression Data Through Modified Agglomerative M-CURE Hierarchical Algorithm

    E. Kavitha1,*, R. Tamilarasan2, N. Poonguzhali3, M. K. Jayanthi Kannan4

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1027-141, 2022, DOI:10.32604/csse.2022.020634

    Abstract Gene expression refers to the process in which the gene information is used in the functional gene product synthesis. They basically encode the proteins which in turn dictate the functionality of the cell. The first step in gene expression study involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise and imprecision. For a biological system to function, the essential cellular molecules must interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will… More >

  • Open Access


    Improved KNN Imputation for Missing Values in Gene Expression Data

    Phimmarin Keerin1, Tossapon Boongoen2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 4009-4025, 2022, DOI:10.32604/cmc.2022.020261

    Abstract The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is… 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 >

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