A Faster cDNA Microarray Gene Expression Data Classifier for Diagnosing Diseases

Hsieh, Sun-Yuan; Chou, Yu-Chun

doi:10.1109/tcbb.2015.2474389

Cited by 17 publications

(6 citation statements)

References 34 publications

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“…Hsieh and Chou (2016) have proffered a GEG-based classifier for reducing the computational time during data classification. The introduced classifier filtered the genes with an edge weight for defining the importance of genes, thus simplified the accurate comparison and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

Eluri

Kancharla

Dara³

et al. 2021

DTA

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PurposeGene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.Design/methodology/approachThe proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.FindingsThe proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.Originality/valueThis paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

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Section: Literature Reviewmentioning

confidence: 99%

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

Eluri

Kancharla

Dara³

et al. 2021

DTA

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“…This performance factor is used for accurately rejecting the false positive cases that could offer anomaly in the outcome. The calculation of specificity is carried out in following manner: (7) In the above expression, the variable A represent number of true negatives while B represents number of false negative. The graphical outcome of specificity is as follows in Figure 10 and Figure 11.…”

Section: Specificitymentioning

confidence: 99%

SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fuzzy Cluster based Nearest Neighbor Classifier

Sudha¹,

Girijamma²

2018

IJECE

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In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.

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“…The microarray gene data classification process is complex because of a larger number of genes in the microarray dataset. 4 The prediction accuracy increases with the help of expression profiles during hidden pattern finding. The most popular issue is the curse of dimensionality and the dimensionality minimization method is commonly divided into two types such as (a) feature extraction and (b) feature selection.…”

Section: Introductionmentioning

confidence: 99%

Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

Nguyen

Trần

et al. 2020

Numer Methods Biomed Eng

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One of the challenging tasks in the medicinal field is genomic disorder investigation and its classification from the microarray dataset. The microarray dataset reorganization and its classification is more complex and expensive in the biomedical research area due to the larger number of features in the microarray dataset. In this paper, we construct a hybrid feature selection method such as t test, Fisher ration, and Bayesian logistic regression to select genes and that reduce the time cost. Based on the features, the top‐ranked features are selected via the best hybrid rank method. Thereafter, the features are extracted using the modified firefly optimization‐based discriminant independent component analysis (MF‐DICA). Especially, the modified firefly optimization algorithm is capable of improving the search efficiency of DICA. From the high dimensional microarray dataset, MF‐DICA is used to obtain the best features within the entire search space. The kernel extreme learning machine classifies the gene features depending upon the most relevant class. Experimentally, six datasets namely Leukemia dataset, Diffuse Larger B‐cell Lymphomas, Lung cancer, Breast cancer, Prostate tumor, and Colon dataset are chosen to evaluate the performance of proposed approaches. Finally, the experimental data demonstrate that the proposed method is well suitable to classify the microarray data.

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A Faster cDNA Microarray Gene Expression Data Classifier for Diagnosing Diseases

Cited by 17 publications

References 34 publications

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fuzzy Cluster based Nearest Neighbor Classifier

Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

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