Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Hilal, Anwer Mustafa; Malibari, Areej; Obayya, Marwa; Alamgeer, Mohammad; Mohamed, Abdullah; Motwakel, Abdelwahed; Yaseen, Ishfaq; Hamza, Manar Ahmed; Zamani, Abu Sarwar

doi:10.1155/2022/1698137

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Furthermore, the study was not employed to binary classification that may achieved less accuracy. Hila et al [16] presented a novel feature subset selection technique for gene expression classification that makes use of an adaptive neuro-fuzzy inference system. Four independent microarray gene expression datasets-Leukemia, Prostate Cancer, DLBC Stanford, and Colon Cancer-each linked to a distinct type of cancer were used in the analysis to evaluate this methodology.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

2024

IJFMR

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The vast dimensionality of gene expression data and the limited number of relevant genes necessitate the adoption of gene selection techniques. Furthermore, the choice of an efficient classifier plays a pivotal role in achieving accurate results. In this study, we employ the Minimum Redundancy Maximum Relevance (mRMR) method for gene selection, coupled with ensemble classifiers and individual classifiers like K-Nearest Neighbors (KNN) and Decision Trees (DT). A comparative analysis between two ensemble classifiers and two individual classifiers is conducted, revealing the superior performance of the ensemble classifiers. Our investigation utilizes four distinct cancer gene expression datasets to showcase the efficacy of employing ensemble classifiers and gene selection methods for cancer classification. The ensemble classifier (Bagging Classifier), in conjunction with the MRMR method selecting only the top 30 genes, achieved an impressive overall accuracy of 94% across all four employed datasets.

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Section: Related Workmentioning

confidence: 99%

Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

2024

IJFMR

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“…Hilal et al [9], suggested a novel feature subset selection with an optimum adaptive neuro-fuzzy inference system (FSS-OANFIS) for cancer classification. The colon cancer dataset produced the best results, with 89.47%, 87.80%, 87.82%, and 87.82% for accuracy, sensitivity, specificity, and G-measure, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Novel Fuzzy Classifier Model for Cancer Classification Using Gene Expression Data

Khalsan,

Mu,

Al-Shamery

et al. 2023

IEEE Access

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In the pursuit of better cancer classification, many studies have been conducted to identify the genes associated with cancer. However, the high dimensionality of gene expression data and the limited relevance of a few genes pose significant challenges to this endeavor. Existing gene selection methods yield divergent gene lists, further complicating the classification process. To overcome this issue, we developed a novel approach called Fuzzy Gene Selection (FGS), which combines the strengths of various gene selection methods in the field. FGS was developed using three feature selection techniques (Mutual Information, F-ClassIf, and Chi-squared) to rank genes based on their importance. These methods generated scores and rankings for each gene. Fuzzification and Defuzzification techniques were then applied to combine these scores into a single best score for each gene. This approach aids in identifying genes of significance in cancer classification, especially in multi-class scenarios. Classifiers often produce convergent decisions in such cases, where the predicted probabilities for different classes do not always correspond to the correct predicted class with the highest probability. To address this, we developed a novel Fuzzy classifier that leverages the contributions from each node's traditional deep classifier. This novel approach combines the strengths of traditional deep classifiers at individual nodes, enabling the Fuzzy classifier to make more robust and informed predictions. The Fuzzy classifier (FC) has demonstrated improvements in accuracy and the generalization of the proposed algorithm to accurately classify different cancer types.INDEX TERMS Classifier methods, Fuzzy gene selection technique, and Fuzzy classifier method.

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Multi-functional scar tissue discrimination platform construction and exploration of molecular mechanism for scar formation

Hu,

Yu,

Kong

et al. 2024

Appl Intell

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Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Cited by 5 publications

References 27 publications

Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

Enhancing Cancer Classification Through Ensemble Machine Learning and Gene Selection Approaches

A Novel Fuzzy Classifier Model for Cancer Classification Using Gene Expression Data

Multi-functional scar tissue discrimination platform construction and exploration of molecular mechanism for scar formation

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