Gene expression based cancer classification

Tarek, Sara; Elwahab, Reda Abd; Shoman, Mahmoud

doi:10.1016/j.eij.2016.12.001

Cited by 57 publications

(39 citation statements)

References 7 publications

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“…Here we can find that SVM method has the highest accuracy. The authors [7] proposed the different classifier to colon cancer gene expression data set. In paper [8], a work on the microarray data of the human genome sequence of the colon cancer and leukemia patient.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Technique for Detecting NSCLC

Hegde*¹,

Hegde²,

Chetan³

et al. 2019

IJRTE

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Abstract: The lung cancer is one of the major cancers in the world. In lung cancer we have two main types. They are small cell lung cancer and non-small cell lung cancer. In this paper we mainly concentrated on the detection of non-small cell lung cancer. There are several types in NSCLC and we have several stages in NSCLC. The flow of proposed paper consist the following steps: (1) Background: Here we describe the different types of lung cancer and mainly about NSCLC; (2) Methods: To find the NSCLC, we are using the Recurrent Neural Network (RNN); (3 ) Results: After the training and prediction of the model, we will get the final result as weather the given patient suffering from NSCLC or not; and (4) Conclusions: The given model is working for all the possible datasets and the training accuracy is 88%. The accuracy of the model is mainly depends on the epoch value. For ideal epoch value the accuracy of the model is high.Dataset: The datasets are taken from the NCBI website. We have used the nucleotide datasets of the NCBI website. The datasets are open source and easily accessible. We have used the DNA sequence data of the human genome data. All the NSCLC patients data are taken as positive data and human reference gene data are taken as negative data.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Technique for Detecting NSCLC

Hegde*¹,

Hegde²,

Chetan³

et al. 2019

IJRTE

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“…[20] Poisson-based measures and K-means clustering algorithm is to group tags with similar count profiles across libraries. An effective ensemble [21] approach is proposed. Ensemble classifier increases the performance of the classification, and also improves the confidence of the results.…”

Section: Computational Analysis Of Gene Expression:-mentioning

confidence: 99%

Computational Analysis on Gene Expression Pattern: A Survey.

Vimala¹,

Usha²

2018

IJAR

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“…Classification of cancer by molecular level has gained the interest of researchers because it provides the diagnosis of disease in a more systematic, accurate and objective manner for several types of cancer. An ensemble system, a set of individually trained classifiers presented in [2] whose decisions integrates with majority voting, weighted voting and Naïve Bayes combination.…”

Section: Introductionmentioning

confidence: 99%

“…The ensemble method in [2] also provided solutions enhancing the accuracy of the result, applied ensemble technique to more cancer types, and avoiding the problems related to over fitting. However, to improve tumour classification accuracy, different classifiers have to be used as base members.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection

Bose*¹,

Christopher²

2019

IJRTE

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Tumour detection medical applications utilize classification techniques to categorize malicious and nonmalicious tumour features to provide an efficient medical diagnosis of the human individual under investigation. One way to enable efficient classification, Feature extraction methods are used to eliminate the redundant features and obtain the most relevant features. However, the challenges concerning the dimension and quantum of tumour dataset persist. Toward this goal, this paper aims to maximize the malicious tumour classification accuracy using two reliable ensemble classifiers namely Bootstrap Aggregation and k-nearest neighbour. Tumour features extracted by Aggregate Linear Discriminate Analysis (LDA) and the feature distance is calculated with iterative scattering matrix algorithm. The extracted features are further refined by aggregation to select most effective feature values. After this, an ensemble classifier technique is employed to construct malicious and non-malicious tumour classes. The tumour classification based on an ensemble of bagging and knearest neighbour. Simulation is carried out on Tumour Repository data set to show that proposed ensemble classifiers have considerably better tumour detection accuracy than existing conventional techniques. Numerical performance evaluations show that 8% improvement by proposed method in tumour classification accuracy for malicious tumour detection in human individuals.

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Gene expression based cancer classification

Cited by 57 publications

References 7 publications

Deep Learning Technique for Detecting NSCLC

Deep Learning Technique for Detecting NSCLC

Computational Analysis on Gene Expression Pattern: A Survey.

Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection

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