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A Classification Framework Applied to Cancer Gene Expression Profiles

Hussein Hijazi, Christina Chan
2013 Journal of Healthcare Engineering  
However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers.  ...  The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification  ...  ACKNOWLEDGEMENTS This research was supported in part by the US National Institutes of Health (NIH) (R01GM079688 and 1R01GM089866), and the US National Science Foundation (NSF) (CBET 0941055 and DBI 0701709  ... 
doi:10.1260/2040-2295.4.2.255 pmid:23778014 pmcid:PMC3873740 fatcat:czpwwm2jc5hojnmcup3nm3yrgq

EXPLORING FEATURES AND CLASSIFIERS TO CLASSIFY GENE EXPRESSION PROFILES OF ACUTE LEUKEMIA

SUNG-BAE CHO
2002 International journal of pattern recognition and artificial intelligence  
Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification.  ...  Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection.  ...  The gain ratio criterion selects the test X so that the gain ratio(X) is maximized. k-nearest neighbor k-nearest neighbor (KNN) is one of the most common methods among memory based induction.  ... 
doi:10.1142/s0218001402002015 fatcat:d3gljkjvcvcehcbsnjrpmxl2hm

Towards Optimal Feature and Classifier for Gene Expression Classification of Cancer [chapter]

Jungwon Ryu, Sung-Bae Cho
2002 Lecture Notes in Computer Science  
Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification.  ...  In this paper, we attempt to explore the optimal features and classifiers through a comparative study with the most promising feature selection methods and machine learning classifiers.  ...  As classifiers, we have adopted multilayer perceptron, self-organizing map, decision tree and k-nearest neighbor.  ... 
doi:10.1007/3-540-45631-7_41 fatcat:vtlonbiytneordpiy245f4xpsm

Optimised feature selection for early cancer detection

K.R. Uthayan, S. Mohanavalli, B. Nivetha, S. Dhivya
2021 Genetika  
As a proof of concept, we present a combination of feature selection techniques that can effectively reduce the feature set and optimize the classification techniques.  ...  Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) status report for the year of 2020, suggests the occurrence of 10.0 million cancer deaths and 19.3 million new cancer cases.  ...  K-Nearest Neighbor The K-Nearest Neighbor approach is different from the other methods considered here in that it uses the data directly for classification rather than first building a model.  ... 
doi:10.2298/gensr2103297u fatcat:idihxdhacjdqpni77ci3kgomhy

Machine Learning Based Approaches For Cancer Classification Using Gene Expression Data

Amit Bhola, Arvind Kumar Tiwari
2018 Zenodo  
We have also evaluated and introduced various proposed gene selection method. In this paper, several issues related to cancer classification have also been discussed.  ...  This paper present an overview of various cancer classification methods and evaluate these proposed methods based on their classification accuracy, computational time and ability to reveal gene information  ...  K-Nearest Neighbour K-nearest-neighbor classifier uses the same distance metric.  ... 
doi:10.5281/zenodo.1207823 fatcat:oxyrmtuqyfa67ezh7p3fdouziu

Evolutionary Computation for Optimal Ensemble Classifier in Lymphoma Cancer Classification [chapter]

Chanho Park, Sung-Bae Cho
2003 Lecture Notes in Computer Science  
For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification.  ...  In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset.  ...  K-nearest neighbor. K-nearest neighbor (KNN) is one of the most common methods for memory based induction.  ... 
doi:10.1007/978-3-540-39592-8_74 fatcat:2fqdifecurbd7oehbghsvds7o4

Neural Network Techniques for Cancer Prediction: A Survey

Shikha Agrawal, Jitendra Agrawal
2015 Procedia Computer Science  
In this paper, we are surveying various neural network technologies for classification of cancer.  ...  Cancer is a dreadful disease. Millions of people died every year because of this disease. It is very essential for medical practitioners to opt a proper treatment for cancer patients.  ...  Support vector machine (SVM), k-nearest neighbor (k-NN) and Probabilistic Neural Network (PNN) are used as classifiers. They used 4 microarray datasets.  ... 
doi:10.1016/j.procs.2015.08.234 fatcat:mej6gtx5gzhz3ei27koahpgn4e

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

Anne-Laure Boulesteix, Carolin Strobl
2009 BMC Medical Research Methodology  
We conclude that the strategy to present only the optimal result is not acceptable, and suggest alternative approaches for properly reporting classification accuracy.  ...  The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and  ...  Acknowledgments This work was partially supported by the Porticus Foundation in the context of the International School for Technical Medicine and Clinical Bioinformatics.  ... 
doi:10.1186/1471-2288-9-85 pmid:20025773 pmcid:PMC2813849 fatcat:is5zvmxhibcifbczjijubdqggy

Dataset complexity can help to generate accurate ensembles of k-nearest neighbors

Oleg Okun, Giorgio Valentini
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
A new ensemble method is proposed that combines predictions of a small number of k-nearest neighbor (k-NN) classifiers with majority vote.  ...  Gene expression based cancer classification using classifier ensembles is the main focus of this work.  ...  As a classifier, a k-nearest neighbor (k-NN) was chosen in this work because it performed well for cancer classification, compared to more sophisticated classifiers [3] .  ... 
doi:10.1109/ijcnn.2008.4633831 dblp:conf/ijcnn/OkunV08 fatcat:mcttmo5inbcdbhibfchzhbhv3u

An integrative genomic and proteomic approach to chemosensitivity prediction

Guo
1992 International Journal of Oncology  
New computational approaches are needed to integrate both protein expression and gene expression profiles, extending beyond the correlation analyses of gene and protein expression profiles in the current  ...  Classifiers of the complete range of drug response (sensitive, intermediate, or resistant) were generated for the evaluated anti-cancer drugs, one for each agent.  ...  Guo was supported by the NIH/NCRR P20 RR16440-03 grant and West Virginia University Research Development Grant (RDG NT10017W). Dr Xianglin Shi is supported by the NIH/NCI 1R01CA119028-01grant.  ... 
doi:10.3892/ijo_00000134 pmid:19082483 pmcid:PMC2667126 fatcat:uj3i6ifhavaz5orveh3v4lnp6y

Stepwise classification of cancer samples using clinical and molecular data

Askar Obulkasim, Gerrit A Meijer, Mark A van de Wiel
2011 BMC Bioinformatics  
We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors.  ...  Results: We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data.  ...  We would like to thank the anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.1186/1471-2105-12-422 pmid:22034839 pmcid:PMC3221726 fatcat:kbrtjtuqnva65ky5lbsagbokoe

DATA MINING FOR GENE EXPRESSION PROFILES FROM DNA MICROARRAY

SUNG-BAE CHO, HONG-HEE WON
2003 International journal of software engineering and knowledge engineering  
To precisely classify cancer we have to select genes related to cancer because the genes extracted from microarray have many noises.  ...  feature selection methods and machine learning classifiers.  ...  Information gain and Pearson's correlation coefficient are the top feature selection methods, and MLP and KNN are the best classifiers.  ... 
doi:10.1142/s0218194003001469 fatcat:npjjcocfcnejjozrumwwbgbhjq

Breast Cancer Type Classification Using Machine Learning

Jiande Wu, Chindo Hicks
2021 Journal of Personalized Medicine  
We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the  ...  models for classifying the two types of breast cancer.  ...  Acknowledgments: The authors wish to thank the participants who donated the samples to the TCGA project used to generate the data used in this project, technical support from TCGA and GDC staff as well  ... 
doi:10.3390/jpm11020061 pmid:33498339 pmcid:PMC7909418 fatcat:xirojodfcneqze7ymi7k4dlfa4

A Hybrid Reduction Approach for Enhancing Cancer Classification of Microarray Data

Abeer M., Basma A.Maher
2014 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
The proposed approach is integrated with two ML classifiers; Knearest neighbor (KNN) and support vector machine (SVM); for mining microarray gene expression profiles.  ...  The strategy to select genes only from the training samples and totally excluding the testing samples from the classifier building process is utilized for more accurate and validated results.  ...  ACKNOWLEDGMENT The authors would like to thank Prof.Abdel Badeeh M.Salem and Prof.El-Sayed M.El-horbaty for their help, care and advices during this research.  ... 
doi:10.14569/ijarai.2014.031001 fatcat:v2ar5qeswjcpzhcpal2q3sg2ei

Quantization and similarity measure selection for discrimination of lymphoma subtypes under k-nearest neighbor classification

Cristian Mircean, Ioan Tabus, Jaakko Astola, Tohru Kobayashi, Hiroshi Shiku, Motoko Yamaguchi, Ilya Shmulevich, Wei Zhang, Dan V. Nicolau, Ramesh Raghavachari
2004 Microarrays and Combinatorial Techniques: Design, Fabrication, and Analysis II  
The proposed technique combines the k -Nearest Neighbor (k -NN) algorithm with optimized data quantization.  ...  Molecular classification of tumors holds great potential for cancer research, diagnosis, and treatment.  ...  The k-Nearest Neighbor method of classification The k-Nearest Neighbor (k-NN) algorithm is conceptually very simple and intuitive: given a data set containing labelled vectors (vectors with known classifications  ... 
doi:10.1117/12.529580 fatcat:buxusphchzaipexkt375s6m3ya
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