Semi-supervised Ensemble Learning for Efficient Cancer Sample Classification from miRNA Gene Expression Data
暂无分享,去创建一个
[1] Aik Choon Tan,et al. Ensemble machine learning on gene expression data for cancer classification. , 2003, Applied bioinformatics.
[2] M. Johnson,et al. Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.
[3] Nesma Settouti,et al. Random forest in semi-supervised learning (Co-Forest) , 2013, 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA).
[4] Michelangelo Ceci,et al. Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering , 2020, BMC Bioinformatics.
[5] Salvatore Alaimo,et al. ncPred: ncRNA-Disease Association Prediction through Tripartite Network-Based Inference , 2014, Front. Bioeng. Biotechnol..
[6] Peter Bühlmann,et al. Bagging, Boosting and Ensemble Methods , 2012 .
[7] Gang Wang,et al. SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media , 2017, Artif. Intell. Medicine.
[8] Anindya Halder,et al. Active Learning Using Fuzzy k-NN for Cancer Classification from Microarray Gene Expression Data , 2015 .
[9] Ziv Bar-Joseph,et al. A Semi-Supervised Method for Predicting Transcription Factor–Gene Interactions in Escherichia coli , 2008, PLoS Comput. Biol..
[10] Sara Tarek,et al. Gene expression based cancer classification , 2017 .
[11] Zhi-Hua Zhou,et al. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[12] José Hernández-Orallo,et al. An experimental comparison of performance measures for classification , 2009, Pattern Recognit. Lett..
[13] Giorgio Valentini,et al. Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.
[14] X. Chen,et al. Random forests for genomic data analysis. , 2012, Genomics.
[15] Zhihua Cai,et al. Erratum to: Evaluation Measures of the Classification Performance of Imbalanced Data Sets , 2009 .
[16] L. Wood,et al. Pancreatic cancer , 2016, The Lancet.
[17] Mohamed A. Ismail,et al. miRNA and gene expression based cancer classification using self-learning and co-training approaches , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.
[18] Anindya Halder,et al. Active Learning Using Fuzzy-Rough Nearest Neighbor Classifier for Cancer Prediction from Microarray Gene Expression Data , 2020, Int. J. Pattern Recognit. Artif. Intell..
[19] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[20] H. Kocher,et al. Pancreatic Cancer , 2019, Methods in Molecular Biology.
[21] Rong Jin,et al. Semi-Supervised Ensemble Ranking , 2008, AAAI.
[22] Marcel Dettling,et al. BagBoosting for tumor classification with gene expression data , 2004, Bioinform..
[23] D. Williamson,et al. The box plot: a simple visual method to interpret data. , 1989, Annals of internal medicine.
[24] Bf Buxton,et al. An introduction to support vector machines for data mining , 2001 .
[25] Bernhard Schölkopf,et al. Semi-Supervised Learning (Adaptive Computation and Machine Learning) , 2006 .
[26] Doina Caragea,et al. Ensemble-based semi-supervised learning approaches for imbalanced splice site datasets , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[27] Anindya Halder,et al. Semi-supervised fuzzy K-NN for cancer classification from microarray gene expression data , 2014, 2014 First International Conference on Automation, Control, Energy and Systems (ACES).
[28] D. Bartel. MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.
[29] Aftab Ali Haider,et al. A Survey of Logic Based Classifiers , 2013 .
[30] Michelangelo Ceci,et al. Learning to Combine miRNA Target Predictions: a Semi-supervised Ensemble Learning Approach , 2014, SEBD.
[31] Tissue microarrays characterise the clinical significance of a VEGF-A protein expression signature in gastrointestinal stromal tumours , 2007, British Journal of Cancer.
[32] Anindya Halder,et al. Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data , 2019, J. Biomed. Informatics.
[33] Zhihua Cai,et al. Evaluation Measures of the Classification Performance of Imbalanced Data Sets , 2009 .
[34] Sung-Bae Cho,et al. An ensemble semi-supervised learning method for predicting defaults in social lending , 2019, Eng. Appl. Artif. Intell..
[35] Martial Hebert,et al. Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.
[36] Tanya Barrett,et al. The Gene Expression Omnibus Database , 2016, Statistical Genomics.
[37] L. Ceriani,et al. The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini , 2012 .
[38] Yongfang Xie,et al. Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application , 2020 .
[39] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[40] Evangelos Kanoulas,et al. Semi-supervised Ensemble Learning with Weak Supervision for Biomedical Relationship Extraction , 2019, AKBC.
[41] Albert Y. Zomaya,et al. A Review of Ensemble Methods in Bioinformatics , 2010, Current Bioinformatics.
[42] Xiaojin Zhu,et al. Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[43] Richard A Armstrong,et al. When to use the Bonferroni correction , 2014, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.
[44] S. Dobson,et al. Genome-wide analysis of the interaction between the endosymbiotic bacterium Wolbachia and its Drosophila host , 2008, BMC Genomics.
[45] Ioannis E. Livieris. A New Ensemble Self-labeled Semi-supervised Algorithm , 2019, Informatica.
[46] Zhi-Hua Zhou. When semi-supervised learning meets ensemble learning , 2011 .
[47] F. Slack,et al. Oncomirs — microRNAs with a role in cancer , 2006, Nature Reviews Cancer.
[48] J. Mendell,et al. MicroRNAs in cell proliferation, cell death, and tumorigenesis , 2006, British Journal of Cancer.
[49] C. Davis,et al. Surgery for colorectal cancer in elderly patients: a systematic review , 2000, The Lancet.
[50] G. U. Ebuh,et al. Modified Wilcoxon Signed-Rank Test , 2012 .
[51] Cha Zhang,et al. Ensemble Machine Learning: Methods and Applications , 2012 .
[52] Jack Y. Yang,et al. A comparative study of different machine learning methods on microarray gene expression data , 2008, BMC Genomics.
[53] Peter Bühlmann,et al. Boosting for Tumor Classification with Gene Expression Data , 2003, Bioinform..
[54] Pablo Guillen,et al. Cancer Classification Based on Microarray Gene Expression Data Using Deep Learning , 2016, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).
[55] Guo Cao,et al. A novel ensemble method for k-nearest neighbor , 2019, Pattern Recognit..
[56] C. Devi Arockia Vanitha,et al. Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection☆ , 2015 .
[57] Michelangelo Ceci,et al. Semi-Supervised Multi-View Learning for Gene Network Reconstruction , 2015, SEBD.
[58] Cha Zhang,et al. Ensemble Machine Learning , 2012 .
[59] Anindya Halder,et al. Ensemble-based active learning using fuzzy-rough approach for cancer sample classification , 2020, Eng. Appl. Artif. Intell..