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  1. 1
    Mutable composite firefly algorithm for gene selection in microarray based cancer classification

    Mutable composite firefly algorithm for gene selection in microarray based cancer classification by Fajila, Mohamed Nisper Fathima

    Published 2022
    “…A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. …”
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    Thesis
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  2. 2
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The aim of this evolution is to reflect the unseen time overhead incurred by optimal real-time algorithm, represented by LRE-TL, which might hinder the claimed optimality of such algorithms when they are practically implemented. …”
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  3. 3
    Machine learning-based leukemia classification using gene expression for accurate diagnosis

    Machine learning-based leukemia classification using gene expression for accurate diagnosis by Mahmoud, Amena, Talpur, Kazim Raza, Saini, Shilpa, Talpur, Bandeh Ali, Shah, Asadullah, Zaki, John

    Published 2025
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  4. 4
    Gene Selection For Cancer Classification Based On Xgboost Classifier

    Gene Selection For Cancer Classification Based On Xgboost Classifier by Teo, Voon Chuan

    Published 2022
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    Undergraduates Project Papers
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  5. 5
    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification

    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fajila, Fathima, Yusof, Yuhanis

    Published 2024
    “…Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. …”
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  6. 6
    Effective gene selection techniques for classification of gene expression data

    Effective gene selection techniques for classification of gene expression data by Yeo, Lee Chin

    Published 2005
    “…Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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  7. 7
    Using fuzzy association rule mining in cancer classification

    Using fuzzy association rule mining in cancer classification by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

    Published 2011
    “…The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. …”
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  8. 8
    Integrated framework with association analysis for gene selection in microarray data classification

    Integrated framework with association analysis for gene selection in microarray data classification by Ong, Huey Fang

    Published 2011
    “…The experimental results showed that the recommended GO based models, KEGG based models, and GO-KEGG based models outperformed the expression-only models by attaining better classification accuracies with less number of genes. …”
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  9. 9
    Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification

    Pathway-based analysis with Support Vector Machine (SVM-LASSO) for gene selection and classification by Nasrudin, Nurul Athirah, Chan, Weng Howe, Mohamad, Mohd Saberi, Deris, Safaai, Napis, Suhaimi, Kasim, Shahreen

    Published 2017
    “…This study proposed a pathway-based analysis for gene classification. Pathway-based analysis enables handling microarray data in order to improve biological interpretation of the analysis outcome. …”
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  10. 10
    Pathway-based analysis with support vector machine (SVM-LASSO) for gene selection and classification

    Pathway-based analysis with support vector machine (SVM-LASSO) for gene selection and classification by Nurul Athirah, Nasrudin, Chan, Weng Howe, Mohd Saberi, Mohamad, Safaai, Deris, Suhaimi, Napis, Shahreen, Kasim

    Published 2017
    “…This study proposed a pathway-based analysis for gene classification. Pathway-based analysis enable handling microarray data in order to improved biological interpretation of the analysis outcome. …”
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  11. 11
    Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification

    Selecting informative genes from leukemia gene expression data using a hybrid approach for cancer classification by Mohamad, Mohd. Saberi, Deris, Safaai, Hashim, Siti Zaiton Mohd.

    Published 2007
    “…We introduce an improved version of hybrid of genetic algorithm and support vector machine for genes selection and classification. …”
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  12. 12
    Gene Selection for Cancer Classification Based on XGBoost

    Gene Selection for Cancer Classification Based on XGBoost by Chuan, Teo Voon, Tomal, Md Raihanul Islam, Moorthy, Kohbalan, Howe, Chan Weng

    Published 2025
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  13. 13
    Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network

    Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network by Zainuddin, Zarita, Pauline, Ong

    Published 2011
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  14. 14
    Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree

    Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree by Arowolo, Micheal Olaolu, Adebiyi, Marion Olubunmi, Adebiyi, Ayodele Ariyo

    Published 2021
    “…The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. …”
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  15. 15
    Filter-Wrapper Methods For Gene Selection In Cancer Classification

    Filter-Wrapper Methods For Gene Selection In Cancer Classification by Alomari, Osama Ahmad Suleiman

    Published 2018
    “…In microarray gene expression studies, finding the smallest subset of informative genes from microarray datasets for clinical diagnosis and accurate cancer classification is one of the most difficult challenges in machine learning task. …”
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  16. 16
    Machine learning-based liver cancer classification using gene expression microarray data

    Machine learning-based liver cancer classification using gene expression microarray data by Mahmoud, Amena, Meraj, Syeda Shaizadi, Saini, Shilpa, Juneja, Sapna, Talpur, Kazim Raza, Shah, Asadullah, Ahmed, Wesam

    Published 2025
    “…This study proposes a supervised machine learning-based approach for liver cancer diagnosis that influences gene expression profiles to achieve an accurate diagnosis. …”
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  17. 17
    Prediction of breast cancer relapse time in continuous scale based on type-2 TSK fuzzy model

    Prediction of breast cancer relapse time in continuous scale based on type-2 TSK fuzzy model by Mahmoudian, Sayed Hamid

    Published 2010
    “…In the first objective of the thesis, a lemma has been proven and a new hybrid algorithm based on Fuzzy Association Rule Mining has been proposed to gather some selected genes and generate fuzzy rules for classification. …”
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  18. 18
    Analyzing RNA-Seq gene expression data using deep learning approaches for cancer classification

    Analyzing RNA-Seq gene expression data using deep learning approaches for cancer classification by Laiqa Rukhsar, Waqas Haider Bangyal, Muhammad Sadiq Ali Khan, Ag Asri Ag Ibrahim, Kashif Nisar, Danda B. Rawat

    Published 2022
    “…In the last phase, classification is performed, and eight DL algorithms are used. …”
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  19. 19
    A new model for iris data set classification based on linear support vector machine parameter's optimization

    A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

    Published 2020
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  20. 20
    Informative top-k class associative rule for cancer biomarker discovery on microarray data

    Informative top-k class associative rule for cancer biomarker discovery on microarray data by Ong, Huey Fang, Mustapha, Norwati, Hamdan, Hazlina, Rosli, Rozita, Mustapha, Aida

    Published 2020
    “…This paper proposes an informative top-k class associative rule (iTCAR) method in an integrative framework for identifying candidate genes of specific cancers. iTCAR introduces an enhanced associative classification algorithm that integrates microarray data with biological information from gene ontology, KEGG pathways, and protein-protein interactions to generate informative class associative rules. …”
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