Semi-supervised Ensemble Learning for Efficient Cancer Sample Classification from miRNA Gene Expression Data

[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..