Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

Yaqoob, Abrar; Verma, Navneet Kumar; Aziz, Rabia Musheer

doi:10.1007/s10916-023-02031-1

Cited by 29 publications

(4 citation statements)

References 65 publications

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“…Gene selection, a significant challenge due to the gene-sample number disparity, requires advanced optimization methods [14,15]. Despite numerous gene selection methods aiming to enhance classification accuracy, further research is needed, particularly for diseases like cancer [16,17]. High classification accuracy is essential for personalized medicine, enabling better physician decision-making and potentially saving lives.…”

Section: Introductionmentioning

confidence: 99%

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Bir-Jmel,

Douiri,

Bernoussi

et al. 2024

Computers

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Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.

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Section: Introductionmentioning

confidence: 99%

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Bir-Jmel,

Douiri,

Bernoussi

et al. 2024

Computers

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“…Traditionally, artificial neural network (ANN) and genetic algorithms have predominantly been used for predictive models [ 2 ]. Recent state of art studies have shown improved classification yields, including gene selection using cuckoo search algorithm [ 3 ], deep learning classification of cancer disease [ 4 ], hybrid feature selection of cancer [ 5 ], marine predator chaotic algorithm [ 6 ], and hybrid cuckoo search with Harris Hawks optimization [ 7 ]. However, such frameworks are unable to determine performance of class variable (process variables) under typical nutrient limitations and complex bioprocessing conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources

Pant,

Pant

2024

PLoS ONE

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The laboratory-scale (in-vitro) microbial fermentation based on screening of process parameters (factors) and statistical validation of parameters (responses) using regression analysis. The recent trends have shifted from full factorial design towards more complex response surface methodology designs such as Box-Behnken design, Central Composite design. Apart from the optimisation methodologies, the listed designs are not flexible enough in deducing properties of parameters in terms of class variables. Machine learning algorithms have unique visualisations for the dataset presented with appropriate learning algorithms. The classification algorithms cannot be applied on all datasets and selection of classifier is essential in this regard. To resolve this issue, factor-response relationship needs to be evaluated as dataset and subsequent preprocessing could lead to appropriate results. The aim of the current study was to investigate the data-mining accuracy on the dataset developed using in-vitro pyruvate production using organic sources for the first time. The attributes were subjected to comparative classification on various classifiers and based on accuracy, multilayer perceptron (neural network algorithm) was selected as classifier. As per the results, the model showed significant results for prediction of classes and a good fit. The learning curve developed also showed the datasets converging and were linearly separable.

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“…Wang et al 28 and Aziz et al 29 proposed methods that reported accuracies over 90%, but the methodologies adopted in these research works are not suitable for highly nonlinear datasets. Yaqoob et al 30 proposed Sine Cosine and Cuckoo Search Algorithm for feature selection and classification of Breast cancer. Low number of prominent genes (30) is selected to attain a classification accuracy of 99%.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

Nair,

Rajaguru,

Karthika

et al. 2024

Sci Rep

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The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

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Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm

Cited by 29 publications

References 65 publications

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

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