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Gene Co-Adaboost: a semi-supervised approach for classifying gene expression data

Published: 01 August 2011 Publication History

Abstract

Co-training has been proved successful in classifying many different kinds of data, such as text data and web data, which have naturally split views. Using these views as feature sets respectively, classifiers could make less generalization errors by maximizing their agreement over the unlabeled data. However, this method has limited performance in gene expression data. The first reason is that most gene expression data lacks of naturally split views. The second reason is that there are usually some noisy samples in the gene expression dataset. Furthermore, some semi-supervised algorithms prefer to add these misclassified samples to the training set, which will mislead the classification. In this paper, a Co-training based algorithm named Gene Co-Adaboost is proposed to utilize limitedly labeled gene expression samples to predict the class variables. This method splits the gene features into relatively independent views and keeps the performance stable by refusing to add unlabeled examples that may be wrongly labeled to the training set with a Cascade Judgment technique. Experiments on four public microarray datasets indicate that Gene Co-Adaboost effectively uses the unlabeled samples to improve the classification accuracy.

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Cited By

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  • (2019)A semi-supervised machine learning framework for microRNA classificationHuman Genomics10.1186/s40246-019-0221-713:S1Online publication date: 22-Oct-2019
  • (2019)Multi-view Co-training for microRNA PredictionScientific Reports10.1038/s41598-019-47399-89:1Online publication date: 29-Jul-2019
  • (2013)miRNA and gene expression based cancer classification using self-learning and co-training approaches2013 IEEE International Conference on Bioinformatics and Biomedicine10.1109/BIBM.2013.6732544(495-498)Online publication date: Dec-2013
  1. Gene Co-Adaboost: a semi-supervised approach for classifying gene expression data

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      cover image ACM Conferences
      BCB '11: Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
      August 2011
      688 pages
      ISBN:9781450307963
      DOI:10.1145/2147805
      • General Chairs:
      • Robert Grossman,
      • Andrey Rzhetsky,
      • Program Chairs:
      • Sun Kim,
      • Wei Wang
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      Publication History

      Published: 01 August 2011

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      Author Tags

      1. cascade judgment
      2. co-training
      3. gene Co-Adaboost
      4. gene features split
      5. multi-views

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      View all
      • (2019)A semi-supervised machine learning framework for microRNA classificationHuman Genomics10.1186/s40246-019-0221-713:S1Online publication date: 22-Oct-2019
      • (2019)Multi-view Co-training for microRNA PredictionScientific Reports10.1038/s41598-019-47399-89:1Online publication date: 29-Jul-2019
      • (2013)miRNA and gene expression based cancer classification using self-learning and co-training approaches2013 IEEE International Conference on Bioinformatics and Biomedicine10.1109/BIBM.2013.6732544(495-498)Online publication date: Dec-2013

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