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Microarray data play a huge role in recognizing a proper cancer diagnosis and classification. In most microarray data set consist of thousands of genes, but the majority number of genes are irrelevant to the diseases. An efficient algorithm for gene selection becomes important to deal with large microarray data. The main challenge is to analyze and...
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... is to pre-search for the optimal genes to be used in the next stage of the process. It is an extra credit for the cancerous genes (highlighted G3) to be identified after mutation as shown in Figure 2. ...
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Citations
... In the study [37], another wrapper feature selection-based method was proposed using cuckoo search with evolutionary operators. This study also used CV-10 for model evaluation. ...
Recent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. However, some issues remain partially resolved, such as suboptimal aggregation methods and non-optimised underlying FS techniques. This study proposed the logarithmic rank aggregate (LRA) method to improve feature aggregation in EFS. Additionally, a hybrid aggregation framework was presented to improve the performance of the proposed method by combining it with several methods. Furthermore, the proposed method was applied to the feature rank lists obtained from the optimised FS technique to investigate the impact of FS technique optimisation. The experimental setup was performed on five binary microarray datasets. The experimental results showed that LRA provides a comparable classification performance to mean rank aggregation (MRA) and outperforms MRA in terms of gene selection stability. In addition, hybrid techniques provided the same or better classification accuracy as MRA and significantly improved stability. Moreover, some proposed configurations had better accuracy, sensitivity, and specificity performance than MRA. Furthermore, the optimised LRA drastically improved the FS stability compared to the unoptimised LRA and MRA. Finally, When the results were compared with other studies, it was shown that optimised LRA provided a remarkable stability performance, which can help domain experts diagnose cancer diseases with a relatively smaller subset of genes.
... Othman et al. (2020), introduced innovative operators into a hybrid multi-objective CS algorithm for genomic selection using seven high-dimensional cancer microarray datasets. The experimental results showed that the proposed technique selected fewer relevant genes while outperforming with multi-objective cuckoo search algorithms in terms of performance [35]. Pandey et al. (2020), presented a novel feature selection technique based on binomial cuckoo search metaheuristic. ...
The early detection of cancer is of paramount importance in the medical field, as it can lead to more precise and effective interventions for successful cancer treatments. Cancer datasets typically contain numerous gene expression levels as features but with a limited number of samples. Thus, feature selection is a crucial initial step to streamline prediction algorithms. These selected features, or genes, play a pivotal role in cancer identification, treatment selection, and variation analysis among different techniques. To address this challenge, present two novel methodologies, by combining Cuckoo Search (CS) and Spider Monkey Optimization (SMO), referred to as SMOCS (Cuckoo Search followed by Spider Monkey Optimization) and CSSMO (Spider Monkey Optimization followed by Cuckoo Search). These approaches are designed for harnessing the strengths of both metaheuristic algorithms to identify a subset of genes that aid in early-stage cancer prediction. Additionally, to enhance the accuracy of the both algorithms, we employ a gene expression reduction method known as minimum Redundancy Maximum Relevance (mRMR) to reduce redundancy in cancer datasets. Subsequently, these gene subsets are classified using Deep Learning (DL) to identify distinct groups or classes associated with specific cancer types. We evaluate the performance of our proposed approaches using six different cancer datasets, assessing cancer sample classification and prediction through metrics such as Recall, Precision, F1-Score, and confusion matrix analysis. Our gene selection methods, in conjunction with DL, achieves significantly improved prediction accuracy when applied to large gene expression datasets compared to existing Deep Learning (DL) and Machine learning models. Experimental results shows that both SMOCS and CSSMO tend to classify cancer with high prediction accuracy, but SMOCS algorithm gives higher prediction accuracy for all the six datasets used with a maximum accuracy of 100%.
... Biologists utilize microarray technology to assess gene expression levels in specific organisms. A microarray data analysis primarily involves identifying optimal treatments for various diseases and achieving precise medical diagnoses through practical applications involving diverse sets of genes [21,25]. However, microarray technologies yield complex gene expression data that pose issues. ...
In this article, Autism Spectrum Disorder (ASD) is discussed, with an emphasis placed on the multidimensional nature of the disorder, which is anchored in genetic and neurological components. Identifying genes related to ASD is essential to comprehend the mechanisms that underlie the illness, yet the condition's complexity has impeded precise information in this field. In ASD research, the analysis of gene expression data helps choose and categorize significant genes. The study used microarray data to provide a novel approach that integrated gene selection techniques with deep learning models to improve the accuracy of ASD prediction. It offered a detailed comparative examination of gene selection approaches and deep learning architectures, including singular value decompositions (SVD), principal component analyses (PCA), and convolutional neural networks (CNNs). This paper combines gene selection methods (PCA and SVD) with deep learning models (CNN) to improve ASD prediction. Compared to more traditional approaches, the study revealed that its integrated methodology was more effective in improving the accuracy of ASD prediction results through experimentation. There was a difference in the accuracy between the PCA-CNN model, which achieved 94.33% with a loss of 0.4312, and the SVD-CNN model, which achieved 92.21% with a loss less than or equal to 0.3354. These discoveries help in the development of more accurate diagnostic and prognostic tools for ASD, which is a complicated neurodevelopmental disorder. Additionally, they provide insights into the molecular pathways that underlie ASD.
... Using seven high dimensional cancer microarray datasets that are freely available, the suggested method was assessed. According to the experimental findings, the suggested technique selected fewer relevant genes while outperforming multi-objective cuckoo search and classic cuckoo search algorithms in terms of performance [28]. Scaria et al. proposed a user-friendly rule-based classification model for processing microarray gene data. ...
... The odyssey commences with the stochastic creation of a population of these virtual cuckoo eggs [39]. As the quest progresses, select cuckoo eggs undergo replacement, being supplanted by novel solutions engendered through a stochastic random walk process, an analogue to the reproductive strategies of the avian inspiration [27,28,38]. To further augment its exploratory prowess, CSA integrates a Levy flight strategy, orchestrating the construction of fresh solutions designed to liberate the algorithm from local optima entrapment, thus facilitating a more comprehensive traversal of the solution [25,26,39]. ...
Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.
... Despite these alarming statistics, the absence of a reliable pre-symptomatic detection method underscores the critical need for early intervention in disease progression [11]. Microarray technology is used to identify genes linked to AD, enabling biologists to evaluate gene expression levels, identify optimal treatments, and make accurate medical diagnoses [12][13][14]. Microarray data analysis presents challenges, including redundancy and overfitting due to the large number of genes and samples considered, requiring careful elimination and mitigation [15]. Scientists use the "gene selection approach" to identify relevant genes for disease classification, reducing computational costs and improving efficiency, particularly in AD [16]. ...
Alzheimer’s disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer’s disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer’s disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA–CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD–CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.
... A generic technique is employed in stage -1, where rated characteristics are selected and a limited subset of them offer more accurate classification results at the end of stage-2.The multi-objective genetic algorithm with Pareto-solutions assists in locating the smallest gene subset. The genetic algorithm is an extremely effective and robust search algorithm [4] [5]. ...
... The wrapper feature selection method applied a time-varying transfer function with the Binary Jaya algorithm to find the optimal subset of features. While the authors in [63] have proposed and developed a hybrid Multi-objective Cuckoo Search with Evolutionary Operators (MOCS-EO) for gene selection. Whereas, the cuckoo search algorithm uses the Pareto optimal technique, and the evolutionary operators that are used double mutation and single crossover operators. ...
... In this part of our experiments, the performance of our proposed algorithm is evaluated against state-of-the-art gene selection algorithms published in the literature in terms of classification accuracy and the number of selected genes, which are shown in Table 4, such as PCC-BPSO and PCC-GA [37], RFA3 [27], and DRF0-CFS [8], XGBoost and XGBoost-MOGA [25], MOCS-EO [63], and TOPSIS-Jaya [11]. Table 4 shows the classification accuracy and the number of selected genes of our algorithm compared to eight other methods on eight microarray datasets. ...
Cancer classification based on microarray data plays a very important role in cancer diagnosis and detection. Indeed, since microarray data contains a huge number of genes and a small number of samples, it is also nonlinear and noisy, which has led to the need to find a way to reduce the data dimensionality. In order to solve this problem, we need to find an effective way to help biologists and medical research scientists. This paper proposes a new bio-inspired algorithm for cancer classification in gene selection called Binary Grey Wolf Optimization Algorithm (BGWOA), which is based on hybridization between Minimum Redundancy-Maximum Relevance (MRMR) and a novel Binary Grey Wolf algorithm. The BGWOA is composed of two stages: The first stage consists of the MRMR pre-filter to obtain the set of relevant genes that reduces the dimensionality of the data sets. The second stage consists of a new Binary Grey Wolf algorithm based on direct similarity and centroid known in the geometric field to update the positions of grey wolves in order to exploit and explore the search spaces. As well, we used a fitness function that depends on the SVM with LOOCV classifier and the rate of unselected genes to evaluate the presented solutions. The primary goal of the last stage is to identify the best relevant subset of genes among those obtained in the first stage. This research used eight microarray datasets to evaluate and compare the proposed method with other existing algorithms. The experimental results produced in this research are able to provide a higher classification accuracy with fewer genes compared to many recently published algorithms. Specifically, the proposed method achieves 100% classification accuracy in five reference datasets with a number of genes ranging from 12 to 25. Therefore, this indicates that our research is promising and significant.
... Using seven high dimensional cancer microarray datasets that are freely available, the suggested method was assessed. According to the experimental findings, the suggested technique selected fewer relevant genes while outperforming multi-objective cuckoo search and classic cuckoo search algorithms in terms of performance[35].Pandey et al. presented a novel feature selection technique based on the binomial cuckoo search metaheuristic was described in the study. To increase stability, a hybrid data transformation procedure was developed that combines Fast Independent Component Analysis (FICA) and Principal Component Analysis (PCA). ...
Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of prediction algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer prediction. First, we made alteration of the fitness of Spider Monkey Optimization (SMO) with Cuckoo search (CS) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using Deep Learning (DL) to identify different groups or classes related to a particular cancer disease. Six different datasets have been utilized to analyze the performance of the proposed approach in terms of cancer sample classification and prediction with Recall, Precision, F1-Score, and confusion matrix. The proposed gene selection method with DL achieves much better prediction accuracy than other existing Deep Learning (DL) and Machine learning models with large gene expression dataset.
... Gene expression data normally receives thousands of genes (sample). Therefore, these data are well known for their high, detailed, and broad range of detail [19]. Microarray evidence was instrumental in cancer detection and classification. ...
... In terms of high-quality solutions, CS trumps PSO and its variants. Othman et al. [73] suggested a cuckoo search algorithm based on bioinspired principles that prioritize key genes over other genes. As a feature selection strategy, their MOCS-EO algorithm intends to apply a multipurpose approach that minimizes the number of genes while increasing the relevance of selected genes and cancer classes. ...
Background
Bioinspired computing algorithms are population-based probabilistic search optimization approaches inspired by biological evolution and activity. These are highly efficient and can solve several problems based on human, chimpanzee, bird, and insect behavior. These approaches have been proposed by the scientific community over the last two decades for common application to solving bioinformatics design problems.
Materials and Methodology
The advanced search boxes in databases such as PubMed, WoS, Science Direct, IEEE Xplore, and Scopus to conduct this research. Keywords such as "machine learning," "bioinspired computing," "DNA sequence optimization," and "bioinformatics" were used with OR and AND operators. Journal and conference articles were the two types of articles focused on, and other reports and book chapters were removed using the search engine's parameters.
Results
Bioinspired techniques are becoming increasingly popular in computer science, electrical engineering, applied mathematics, aeronautical engineering, and bioinformatics. Parametric comparisons suggest that most classic benchmark approaches can be successfully used by employing bioinspired techniques. 56 % of studies are modification based, 30 % hybrid based, and 14 % multiobjective based.
Conclusion
These algorithms can be used to optimize data sets in bioinformatics due to their capacity to solve real-world challenges and their ability to accurately express sequence quality and evaluate DNA sequence optimization.
