Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

Mishra, Purnendu; Bhoi, Nilamani

doi:10.1016/j.bbe.2021.06.004

Cited by 13 publications

(14 citation statements)

References 47 publications

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“…In this part, the effectiveness of the WECLO Kmeans-ALFPDC algorithm is assessed by executing it in MATLAB 2017b with 5 distinct kinds of tumor-associated genomic corpora. As well, a comparative analysis is performed with the classical algorithms implemented by using the considered corpora, including WECR K-means [16], WECR Kmeans-PDC [17], DDHFC [19], nNMF [21] and EANFIS [23] 1.…”

Section: Resultsmentioning

confidence: 99%

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An Adaptive Local Gravitation-based Optimized Weighted Consensus Clustering for Gene Expression Data Classification

2022

IJIES

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The appropriate categorization of tumors from a vast quantity of Gene Expression Data (GED) is one of the most difficult processes in clinical diagnosis. To combat this challenge, a Weighted Consensus of Lion Optimized K-means Ensemble with Peak Density Clustering (WECLO K-means-PDC) algorithm has been developed that calculates the Symmetric Neighborhood (SN) correlation among Data Points (DPs) using the lion optimization. An SN Graph (SNG) was created to select the number of Cluster Centroids (ClustCenter) at every iteration of clustering. But, it was not suitable if the dataset was sparse, as well as the constant density threshold may influence the distinguishing DPs within the cluster boundaries. In this paper, an Adaptive Local resultant Force neighborhood PDC for WECR K-means (WECLO K-means-ALFPDC) algorithm is proposed, which considers additional measures to determine the symmetric neighborhood correlation among the DPs when the dataset is sparse. The major goal is to consider the distinct variances between the sizes and orientations of the DPs nearer to the ClustCenters and edges. To find such variations, two novel local measures called Centrality (CR) and Coordination (CO) are introduced instead of SNG to select the ClustCenters and obtain more precise clustering for classifying cancer from genomic data. Finally, the test results show that the WECLO K-means-ALFPDC algorithm attains 88.7%, 89.1%, 88.42%, 88.38% and 89.04% accuracy on leukemia, lymphoma, prostate cancer, SRBCT and breast cancer databases, respectively compared to the WECLO K-means-PDC algorithm.

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Section: Resultsmentioning

confidence: 99%

“…An Enhanced ANFIS (EANFIS) scheme was developed [23] to categorize the tumor genes. Initially, the input data was pre-processed by the Ensemble Kalman Filter (EnKF).…”

Section: Literature Surveymentioning

confidence: 99%

An Adaptive Local Gravitation-based Optimized Weighted Consensus Clustering for Gene Expression Data Classification

2022

IJIES

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“…Several hybrid ANFIS classification schemes have been used in medicine including a genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO) algorithm for the diagnosis of type 2 diabetes mellitus (Patil et al, 2021), modified-salp swarm optimization (MSSO-ANFIS) algorithm for the heart disease diagnosis (Khan & Algarni, 2020), a subtractive clustering-based ANFIS (SCANFIS) hybrid system for the Alzheimer diagnosis (Kour et al, 2019). Additionally, an enhanced ANFIS (EANFIS) approach has been used for the classification of cancer genes (Mishra & Bhoi, 2021) whereas ANFIS and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO) and breeding swarm optimization (ANFIS-BS) methods are used for classification of epileptic seizures on electroencephalography (EEG) signals, (Shoeibi et al, 2021).…”

Section: Hybrid Anfis Classification Methods Used In Medicinementioning

confidence: 99%

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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The authors propose a method for breast dynamic contrast enhanced‐magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS‐PSO classifier. The average overall classification power of the proposed hybrid ANFIS‐PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training‐30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS‐PSO for the breast cancer diagnosis with a classification accuracy of 94%.

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“…An hybrid Enhanced ANFIS (EANFIS) with Manta ray Foraging Optimization (MaFO) algorithm has been described in (Mishra & Bhoi, 2021) to increase the classification accuracy and to minimize the convergence time of the learning process. S. Belciug (2020) has developed a method to solve the curse of dimensionality and the curse of sparsity issue by merging the two ML approaches such as Logistic regression and single hidden layer feed forward neural network.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification With Microarray Gene Expression Data Based on Improved Whale Optimization Algorithm

Devi

Prithiviraj

2023

International Journal of Swarm Intelligence Research

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Breast cancer is one of the most common and dangerous cancer types in women worldwide. Since it is generally a genetic disease, microarray technology-based cancer prediction is technically significant among lot of diagnosis methods. The microarray gene expression data contains fewer samples with many redundant and noisy genes. It leads to inaccurate diagnose and low prediction accuracy. To overcome these difficulties, this paper proposes an Improved Whale Optimization Algorithm (IWOA) for wrapper based feature selection in gene expression data. The proposed IWOA incorporates modified cross over and mutation operations to enhance the exploration and exploitation of classical WOA. The proposed IWOA adapts multiobjective fitness function, which simultaneously balance between minimization of error rate and feature selection. The experimental analysis demonstrated that, the proposed IWOA with Gradient Boost Classifier (GBC) achieves high classification accuracy of 97.7% with minimum subset of features and also converges quickly for the breast cancer dataset.

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Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

Cited by 13 publications

References 47 publications

An Adaptive Local Gravitation-based Optimized Weighted Consensus Clustering for Gene Expression Data Classification

An Adaptive Local Gravitation-based Optimized Weighted Consensus Clustering for Gene Expression Data Classification

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

Breast Cancer Classification With Microarray Gene Expression Data Based on Improved Whale Optimization Algorithm

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