Lung cancer prediction from microarray data by gene expression programming

Azzawi, Hasseeb; Hou, Jingyu; Xiang, Yong; Alanni, Russul

doi:10.1049/iet-syb.2015.0082

Cited by 54 publications

(25 citation statements)

References 32 publications

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“…In this research work, MATLAB (version 2018a) was used for experimental simulation with i5 processor and 3.2 GHz. In order to estimate the effectiveness of proposed system, the performance of proposed system was compared with a few existing systems such as, sparse logistic regression with L1/2 regularization [12], GEP multi classification using decomposition schemes [14], and GEP models [16] on GEO dataset. The proposed system performance was evaluated by means of TPR, accuracy, FPR and error rate.…”

Section: Resultsmentioning

confidence: 99%

“…H. Azzawi, J. Hou, Y. Xiang, and R. Alanni, [16] developed a new GEP model for detecting the lung cancer from microarray data. In this research, two gene selection algorithms were utilized to extract the lung cancer related genes.…”

Section: Related Workmentioning

confidence: 99%

“…H. Azzawi, J. Hou, Y. Xiang, and R. Alanni, [16] developed GEP method to detect the lung cancer from microarray data (GEO ID: GSE10245). Here, the developed system achieved 93.36% of accuracy in gene classification.…”

Section: B Quantitative Analysismentioning

confidence: 99%

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Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Patil¹,

Patil²,

Prabhakar³

2019

IJITEE

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Currently, the automatic lung cancer classification remains a challenging issue for the researchers, due to noisy gene expression data, high dimensional data, and the small sample size. To address these problems, an enhanced gene selection algorithm and multiclass classifier are developed. In this research, the lung cancer-related genes (GEO IDs: GSE10245, GSE19804, GSE7670, GSE10072, and GSE6044) were collected from Gene Expression Omnibus (GEO) dataset. After acquiring the lung cancer-related genes, gene selection was carried out by using enhanced reliefF algorithm for selecting the optimal genes. In enhanced reliefF gene selection algorithm, earthmover distance measure and firefly optimizer were used instead of Manhattan distance measure for identifying the nearest miss and nearest hit instances, which significantly lessens the “curse of dimensionality” issue. These optimal genes were given as the input for Multiclass Support Vector Machine (MSVM) classifier for classifying the sub-classes of lung cancer. The experimental section showed that the proposed system improved the classification accuracy up to 3-10% related to the existing systems in light of accuracy, False Positive Rate (FPR), error rate, and True Positive Rate (TPR).

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Patil¹,

Patil²,

Prabhakar³

2019

IJITEE

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show abstract

“…Gene Expression Programming (GEP) [29] is a new evolutionary algorithm, which was widely used for classification and gene selection [30–35]. GEP has two merits: flexibility which makes it easy to implement, and the capability of getting the best solution, which is inspired by the ideas of genotype and phenotype.…”

Section: Introductionmentioning

confidence: 99%

Deep gene selection method to select genes from microarray datasets for cancer classification

et al. 2019

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BackgroundMicroarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis.ResultsThe gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost.ConclusionsWe provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples’ classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

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“…In recent years, with the development of gene expression profiling chip and second-generation high-throughput sequencing technology, the amount of data on lung cancer expression profiles has greatly expanded, which provides the basis for the comprehensive study of differentially expressed genes and their biological functions in lung cancer [6]. In this study, 3 gene expression profiles of lung cancer were selected from the GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) [7] database, and we explored the function of DEGs in the development of lung cancer and its relationship with patient prognosis.…”

Section: Introductionmentioning

confidence: 99%

Differentially Expressed Gene Screening, Biological Function Enrichment, and Correlation with Prognosis in Non-Small Cell Lung Cancer

Huang

Tang

et al. 2019

Med Sci Monit

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Background The aim of this study was to explore the differently expressed genes and pathways in non-small cell lung cancer (NSCLC) and their correlation with the prognosis. Material/Methods Gene expression data series of GSE19804, GSE101929, and GSE33532 were downloaded from the Gene Expression Ominibus (GEO) database. The overlaping differently expressed genes (DEGs) were identified form the above 3 data series. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) were used to analyze the biological functions and signal pathways of DEGs. The protein–protein interaction (PPI) was analyzed thorough Search Tool for the Retrieval of Interacting Gens (STRING). The relationship between the expression of hub genes and the prognosis of patients was analyzed by Kaplan-Meier Plotter online software. Results Twenty-nine DEGs were identified, with 22 upregulated genes and 7 downregulated genes. The enriched biological processes were mainly related to diet-induced thermogenesis and actin filament binding. The KEGG pathways were enriched in calcium signaling, regulation of lipolysis in adipocytes, and PPAR signaling. Two downregulated genes ( MMP1 and SPP1 ) were identified as hub genes by Cytohubba. Twenty-two dysregulated genes were correlated with patient prognosis. Conclusions Differentially expressed genes are common in NSCLC patients and can be used as biomarkers for patient prognosis.

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Lung cancer prediction from microarray data by gene expression programming

Cited by 54 publications

References 32 publications

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Deep gene selection method to select genes from microarray datasets for cancer classification

Differentially Expressed Gene Screening, Biological Function Enrichment, and Correlation with Prognosis in Non-Small Cell Lung Cancer

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