Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Alshamlan, Hala; Badr, Ghada; Al-Ohali, Yousef

doi:10.1016/j.compbiolchem.2015.03.001

Cited by 195 publications

(83 citation statements)

References 24 publications

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“…To date, a large number of biomarkers have been proposed for GBC progression and aggressiveness [28]. Regulation of both coding genes and noncoding RNAs in GBC has been suggested to have considerable potential for predicting the diagnosis and prognosis of patients with GBC[29]. …”

Section: Discussionmentioning

confidence: 99%

HDAC1 promoted migration and invasion binding with TCF12 by promoting EMT progress in gallbladder cancer

Shen

et al. 2016

Oncotarget

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The identification of prognostic markers for gallbladder cancer is needed for clinical practice. Histone deacetylases (HDACs) play an important role in tumor development and progression by modifying histone and non-histone proteins. However, the expression of HDAC1 in patients with gallbladder cancer is still unknown. Here, we reported that HDAC1 expression was elevated in cancerous tissue and correlated with lymph node metastasis and poorer overall survival in patients with GBC. Knockdown of HDAC1 using lentivirus delivery of HDAC1-specific shRNA abrogated the migration and invasion of GBC cells in vitro. TCF-12, as the HDAC1 binding protein, has also correlates with poor prognosis in GBC patients. And there is a positive correlation between HDAC1 and TCF-12 which leading the high invasion and migration ability of GBC cells. Taken together, our data suggested that HDAC1 and TCF-12 are a potential prognostic maker and may be a molecular target for inhibiting invasion and metastasis in GBC.

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Section: Discussionmentioning

confidence: 99%

HDAC1 promoted migration and invasion binding with TCF12 by promoting EMT progress in gallbladder cancer

Shen

et al. 2016

Oncotarget

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“…Recently, ACO algorithms have been developed to solve continuous optimization problems. These problems are characterized by the fact that decision variables have continuous domains, unlike discrete problems [7]. Using a single optimization algorithm has the disadvantages of low accuracy and generalizability in solving complex problems.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Khourdifi¹,

Bahaj²

2019

IJIES

202

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The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

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“…Genetic Bee Colony (Gbc) [25] is a well known algorithm in the field of microarray data analysis. Gbc combines the advantages of two naturally inspired algorithms: Genetic Algorithm and Artificial Bee Colony.…”

Section: Pairwise Evaluation Methodsmentioning

confidence: 99%

“…We compare Pavicd with some of the state-of-the-art feature selection algorithms such as: RELIEFF [17], mRMR [22], Fcbf [23], Cfs [24], INTERACT [26] and Gbc [25]. To run experiments, we implemented mRMR and Gbc algorithms in weka.…”

Section: Empirical Studymentioning

confidence: 99%

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

Angulo

2018

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Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster.

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Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Cited by 195 publications

References 24 publications

HDAC1 promoted migration and invasion binding with TCF12 by promoting EMT progress in gallbladder cancer

HDAC1 promoted migration and invasion binding with TCF12 by promoting EMT progress in gallbladder cancer

Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

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