Gene expression based cancer classification
| Autor: | Sara Tarek, Reda Abd Elwahab, Mahmoud Shoman |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Cancer classification Computer science Microarrays Bioinformatics Feature selection 02 engineering and technology Management Science and Operations Research computer.software_genre Machine learning 03 medical and health sciences Molecular level 0202 electrical engineering electronic engineering information engineering Cancer Training set business.industry QA75.5-76.95 Classification Cancer treatment Computer Science Applications 030104 developmental biology Electronic computers. Computer science 020201 artificial intelligence & image processing Data mining Artificial intelligence Gene expression DNA microarray business computer Classifier (UML) Ensemble K-NN Information Systems |
| Zdroj: | Egyptian Informatics Journal, Vol 18, Iss 3, Pp 151-159 (2017) |
| ISSN: | 1110-8665 |
| DOI: | 10.1016/j.eij.2016.12.001 |
| Popis: | Cancer classification based on molecular level investigation has gained the interest of researches as it provides a systematic, accurate and objective diagnosis for different cancer types. Several recent researches have been studying the problem of cancer classification using data mining methods, machine learning algorithms and statistical methods to reach an efficient analysis for gene expression profiles. Studying the characteristics of thousands of genes simultaneously offered a deep insight into cancer classification problem. It introduced an abundant amount of data ready to be explored. It has also been applied in a wide range of applications such as drug discovery, cancer prediction and diagnosis which is a very important issue for cancer treatment. Besides, it helps in understanding the function of genes and the interaction between genes in normal and abnormal conditions. That is done by monitoring the behavior of genes -gene expression data- under different conditions. In this paper, an effective ensemble approach is proposed. Ensemble classifiers increase not only the performance of the classification, but also the confidence of the results. The motivations beyond using ensemble classifiers are that the results are less dependent on peculiarities of a single training set and because the ensemble system outperforms the performance of the best base classifier in the ensemble. |
| Databáze: | OpenAIRE |
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