Search Results - (( gene selection models algorithm ) OR ( wave optimization max algorithm ))*

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  1. 1
    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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  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
    “…Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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  4. 4
    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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  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
    “…In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. …”
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  6. 6
    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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  7. 7
    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
    “…It is observed that the proposed model for leukemia classification has an accuracy of 97% using SVM algorithm whereas 94% is using Logistic regression algorithm.…”
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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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 achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques.…”
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  12. 12
    A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

    A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications by Uddin I., Awan H.H., Khalid M., Khan S., Akbar S., Sarker M.R., Abdolrasol M.G.M., Alghamdi T.A.H.

    Published 2025
    “…Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. …”
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  13. 13
    Prognosis of early cervical carcinoma using gene expression profiling

    Prognosis of early cervical carcinoma using gene expression profiling by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

    Published 2015
    “…Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.…”
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  14. 14
    Understanding the occurrence of metastatic breast cancer through clinical, phenotype and genotype data, and the employment of machine learning / Nadia Jalaludin

    Understanding the occurrence of metastatic breast cancer through clinical, phenotype and genotype data, and the employment of machine learning / Nadia Jalaludin by Jalaludin, Nadia

    Published 2023
    “…For objective (b), prediction model was generated based on the outcome of (a) by using the Random Forest (RF) algorithm and validated by 5-fold cross validation. …”
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  15. 15
    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 next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. …”
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  16. 16
    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex

    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

    Published 2012
    “…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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  17. 17
    Classification of Immunosignature Using Random Forests for Cancer Diagnosis

    Classification of Immunosignature Using Random Forests for Cancer Diagnosis by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

    Published 2015
    “…We have used the Random Subset gene selection method to avoid overfitting and improve model performance in order to make the input data suitable for the classification stage, which has been implemented using the Random Forest (RF) classifier. …”
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  18. 18
    DNA enhancer prediction using machine learning techniques with novel feature representation

    DNA enhancer prediction using machine learning techniques with novel feature representation by Fong, Pui Kwan

    Published 2016
    “…Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences.…”
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  19. 19
    Process Planning Optimization In Reconfigurable Manufacturing Systems

    Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

    Published 2008
    “…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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  20. 20
    Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

    Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization by Nader Ibrahim Namazi, Sameer Alshehri, Rawan Bafail, Bader Huwaimel, Amal M. Alsubaiyel, Ali H. Alamri, Ahmed D. Alatawi, Hossam Kotb, Mohd Sani Sarjadi, Md. Lutfor Rahman, Mohammed A.S. Abourehab

    Published 2022
    “…ADA-LR was selected as the primary model according to numerical and visual analysis. …”
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