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  1. 1
    A Study On Gene Selection And Classification Algorithms For Classification Of Microarray Gene Expression Data

    A Study On Gene Selection And Classification Algorithms For Classification Of Microarray Gene Expression Data by Yeo, Lee Chin, Deris, Safaai

    Published 2005
    “…In This Paper, A Study On Numerous Combinations Of Gene Selection Techniques And Classification Algorithms For Classification Of Microarray Gene Expression Data Is Presented. …”
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  2. 2
    Gene Selection For Cancer Classification Based On Xgboost Classifier

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

    Published 2022
    “…Gene selection is the technique that applied to the gene selection dataset, such as DNA microarray, which is develop to reduce the less informative gene, so that the selected gene is related to the disease diagnosis. …”
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  3. 3
    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
    “…When classifying tissue samples, gene selection plays an important role. In this research, some existing gene selection techniques are studied and better gene selection techniques are proposed and developed. …”
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  4. 4
    A model for gene selection and classification of gene expression data

    A model for gene selection and classification of gene expression data by Mohamad, Mohd Saberi, Omatu, Sigeru, Deris, Safaai, Mohd Hashim, Siti Zaiton

    Published 2007
    “…A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. …”
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  5. 5
    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
    “…A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. …”
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  6. 6
    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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  7. 7
    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
    “…The wrapper approach encounters problems of complex interactions among genes and stagnation in local optima. To address these drawbacks, this study investigates filter and wrapper methods to develop effective hybrid methods for gene selection. …”
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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
    “…To achieve that, an integrated framework with a new gene selection method was developed to improve classification performance in terms of accuracy and number of selected genes. …”
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  9. 9
    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
    “…A dataset has been taken and thus preprocessed which consist of profiles of gene expression of patients having leukemia. Then the feature selection techniques have been used for classification of informative genes. …”
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    Proceeding Paper
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  10. 10
    The development of semantic meta-database: an ontology based semantic integration of biological databases

    The development of semantic meta-database: an ontology based semantic integration of biological databases by Samsudin, Ruhaidah, Deris, Safaai, Othman, Muhammad Razib, Md. Illias, Rosli

    Published 2007
    “…The tool comprises two intelligent algorithms. The first algorithm combines parallel genetic algorithm with the split-and-merge algorithm. …”
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  11. 11
    VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

    VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS. by FAROOQ, HUMERA

    Published 2012
    “…This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. …”
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  12. 12
    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
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  13. 13
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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  14. 14
    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
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  15. 15
    Multi-stage feature selection in identifying potential biomarkers for cancer classification

    Multi-stage feature selection in identifying potential biomarkers for cancer classification by Wong, Yit Khee, Chan, Weng Howe, Nies, Hui Wen, Moorthy, Kohbalan

    Published 2022
    “…Therefore, this study aims to investigate and develop a better feature selection to identify potential biomarkers from gene expression data and construct a deep neural network classification model using these selected features. …”
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  16. 16
    An improved directed random walk framework for cancer classification using gene expression data

    An improved directed random walk framework for cancer classification using gene expression data by Seah, Choon Sen

    Published 2020
    “…From the perspective of computing, an algorithm is developed to ease the diagnosis process, but the feasibility is not reliable. …”
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  17. 17
    SUDOKU HELPER

    SUDOKU HELPER by Abdul Razak, Muhammad Asyraf

    Published 2015
    “…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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    Final Year Project
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  18. 18
    Linear-pso with binary search algorithm for DNA motif discovery / Hazaruddin Harun

    Linear-pso with binary search algorithm for DNA motif discovery / Hazaruddin Harun by Harun, Hazaruddin

    Published 2015
    “…The Linear-PSO algorithm was the first version of improvement. However due to the longer time required for complete execution of this algorithm, the Binary Search technique was integrated and a new version of the algorithm was developed, namely the Linear-PSO with Binary Search (LPBS) algorithm. …”
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  19. 19
    An enhanced feature selection and cancer classification for microarray data using relaxed Lasso and support vector machine

    An enhanced feature selection and cancer classification for microarray data using relaxed Lasso and support vector machine by Aina Umairah, Mazlan, Noor Azida, Sahabudin, Muhammad Akmal, Remli, Nor Syahidatul Nadiah, Ismail, Adenuga, Kayode I.

    Published 2021
    “…Thus, various feature selection methods have been developed intended to reduce the dimensionality of microarray as well as to select only the most relevant genes. …”
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  20. 20
    An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer

    An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer by Chandran, Suntharaamurthi, Kohbalan, Moorthy, Mohd Arfian, Ismail, Mohd Zamri, Osman, Mohd Azwan Mohamad, Hamza, Ernawan, Ferda

    Published 2018
    “…Improved iRafNet was designed and developed to reduce the computational time to construct the GRN inference gene from the dataset. …”
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