Microarray data analysis for cancer classification

Osareh, Alireza; Shadgar, Bita

doi:10.1109/hibit.2010.5478893

Cited by 31 publications

(23 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later five well known classifier algorithms are utilized (Support Vector Machine (SVM), K Nearest Neighbor (KNN), Naïve Bayes (NB), Neural Network (NN), and Decision Tree (DT)) to classify nine famous available gene expression datasets. After the experiments, it was resulted that in 8 out of 9 datasets, SVMs classifier outperforms KNN, NB, NN and DT obviously in all cases [9].…”

Section: A Bootstrapped Genetic Algorithm and Support Vector Machine mentioning

confidence: 98%

“…It has an advantage applied in cancer diagnostic in that its performance appears not to be affected by using the set of full genes [9]. k-Nearest Neighbor (KNN) is one of the simplest learning algorithms, and applied to a variety of problem.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…A major problem in these microarray data is the high redundancy and the noisy nature of many genes or irrelevant information for accurate classification of cancer. Only a small number of genes may be important [9]. Early and accurate detection and classification of cancer is critical to the wellbeing of patients.…”

Section: Introductionmentioning

confidence: 99%

“…The suggested algorithm resulted in a fast hybrid feature selectors that can solve feature selection problems in high dimensional datasets and select a small subset full of informative genes that is most relative to cancer classification. Another research developed an automated system for robust and reliable cancer diagnoses based on gene microarray data as stated by the authors in [9]. They investigated that support vector machine classifier algorithms outperforms other algorithms such as K nearest neighbors, naive Bayes, neural networks and decision tree; and thus they could adopt the important genes for cancer tumor classifications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algorithms Implemented for Cancer Gene Searching and Classifications

Al-Rajab

2014

Bioinformatics Research and Applications

View full text Add to dashboard Cite

Abstract. Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is conducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification.

show abstract

Section: A Bootstrapped Genetic Algorithm and Support Vector Machine mentioning

confidence: 98%

Section: Classification Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms Implemented for Cancer Gene Searching and Classifications

Al-Rajab

2014

Bioinformatics Research and Applications

View full text Add to dashboard Cite

show abstract

“…On the other hand, there are two major challenges in the analysis of microarray they are small data sample available from few numbers of patients(often less than hundred) and high dimensional dataset(nearly thousands or tens of thousands of genes) [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Microarray Data Analysis for Cancer Classification

Chitode¹,

Nagori²

2013

IJCA

View full text Add to dashboard Cite

Cancer is most deadly human disease. According to WHO 7.6 million deaths (around 13% of all deaths) in 2008 were caused by cancer. A Cancer diagnosis can be achieved with gene expression microarray data. Microarray allows monitoring of thousands of genes of a sample simultaneously. But all the genes in gene expression data are not informative. The relevant gene selection/extraction is the main challenge in microarray data analysis. Microarray data classification is two stage process i.e. features selection and classification. Feature selection techniques are used to extract a small subset of relevant genes without degrading the performance of classifier. The classifier uses these extracted relevant genes for cancer classification. In this review paper there is a comparative study of the feature selection and classification techniques. The evaluation criteria are applied to find out the best combination of feature selection and classification technique for accurate cancer classification General TermsCancer classification

show abstract