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4th International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Vijayeeta, Prachi Kar, Utsav Rana, Madhurima Das, Madhabananda Mishra, B. S. P. KIIT Univ Sch Comp Engn Odisha India

In the late 19th century, the advent of malignant tissues in the human cells has come into limelight. A lot of research works has been carried out to detect the tumorous cells and subsequently, various methodologies have been adopted to distinguish different types of cancerous cells. Gene expression based microarray technique has been used as an efficient and emerging technique in the field of cancer classification, prediction, diagnosis and many other type of research. In this paper,we have used microarray data which is a collection of microscopic spots. Since there is an existence of huge dataset, the dimensionality reduction of the dataset is done at the first stage of our work. At the second stage, classification is performed using supervised learning methods. At the final stage, we have optimized the objective function using soft computing technique which yields the relevant *** computing refers to a consortium of computational methodologies that has motivated many scientific researchers to contribute their efforts in designing highly powerful intelligent systems. We are mainly focusing on the classification of cancerous cells using two classifiers called SVM (support vector machine) and K-NN (K-nearest neighbor) and an optimization technique is being incorporated on significant parameters that results in better and relevant output. (C) 2015 Published by Elsevier B.V. This is an open access article under CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： microarray data Support Vector Machine (SVM) K-NN(K- Nearest Neighbor) Soft computing

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Supervised Machine Learning Approach for microarray Classification of Malignant Tissues using Soft Computing Techniques 1

Supervised Machine Learning Approach for Microarray Classifi...

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International Conference on Computational Intelligence and Networks

作者： Kar, Utsav Rana, Madhurima Vijayeeta, Prachi Das, Madhabananda KIIT Univ Sch Comp Engn Odisha India

ISBN: (纸本)9781479975495

In today's highly integrated world, when solutions to problems are cross disciplinary in nature, Soft computing promises to become a powerful means for obtaining solutions to problem quickly, accurately and acceptably. Soft computing refers to a consortium of computational methodologies that has motivated many scientific researchers to contribute their efforts in designing highly powerful intelligent systems. During the early 20th century, the advent of malignant tissues in the human cells came into knowledge of medical researchers. A herculean task of classifying the tumorous cells became a very challenging task in the gamut of information, especially in the field of intelligent systems. In this work, we have applied Artificial Bee Colony (ABC) optimization along with the supervised learning technique i.e. support vector machine (SVM) to estimate the cost of classification. We have also simulated the cancerous dataset with the implementation of Cat Swarm Optimization (CSO) with SVM. Prior to classification, we have tried our level best to eliminate redundant and unwanted data with the help of Principal Component Analysis (PCA) technique. A great deal of work in predicting feasible as well as global best solutions have been put forward by many innovative and advanced heuristic search strategies.

关键词： microarray data Support Vector Machine (SVM) Soft computing PCA CSO ABC

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Outcomes of Gene Association Analysis of Cancer microarray data are Impacted by Pre-processing Algorithms

Outcomes of Gene Association Analysis of Cancer Microarray D...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Baskaran, N. Kwoh, Chee Keong Hui, Kam M. Natl Canc Ctr Singapore Div Cellular & Mol Res 11 Hosp Dr Singapore 169610 Singapore Nanyang Technol Univ Sch Comp Engn Div Informat Syst Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9781424483075

Gene association analysis of cancer microarray data provides a wealth of information on gene expression patterns and cancer pathways to enhance the identification of potential biomarkers for cancer diagnosis, prognosis, and prediction of therapeutic responsiveness. However, achieving these biological/clinical objectives relies heavily on the functional capabilities and accuracy of the various analytical tools to mine these cancer microarray gene expression profiles. Many preprocessing algorithms exist for analyzing Affymetrix microarray gene expression data. Previous studies have evaluated these algorithms on their capabilities in accurately determining gene expression using a variety of spike-in as well as experimental data sets. However, variations in detecting differentially expressed genes between these different pre-processing algorithms on a single cancer dataset have not been done in a systems-level evaluation. In this study, we assessed the comparability and the level of variation between PLIER, GCRMA, RMA and MAS5 for their capability to detect differentially expressed genes.

关键词： Differentially Expressed Genes GCRMA Gene Expression Profiling Human Hepatocellular Carcinoma (HCC) MAS5 microarray data Preprocessing Algorithms PLIER RMA

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A Gene Selection Method for microarray data Based on Sampling

A Gene Selection Method for Microarray Data Based on Samplin...

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2nd International Conference on Computational Collective Intelligence: Technologies and Applications

作者： Leu, Yungho Lee, Chien-Pang Tsai, Hui-Yi Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei 10607 Taiwan

ISBN: (纸本)9783642167317

microarray technology has become an important tool for biologists in recent years. It can obtain the expressions of a large amount of genes in a single experiment. One of the research issues of microarray is to select a set of relevant genes from a large number of genes to assist clinical diagnosis. In this paper, we propose a method for gene selection in microarray data. In the proposed method, we first classify genes into three different groups of genes according to their expressions in the microarray experiment. Then, we use probability sampling method to generate several candidate subsets of genes. Finally, we use chi(2)-test for homogeneity to select the relevant genes. The experiment results show that the proposed method is better than the other methods in terms of classification accuracy and the number of genes selected.

关键词： Gene selection microarray data Probability sampling chi(2)-test for homogeneity

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Filtering for Improved Gene Selection on microarray data

Filtering for Improved Gene Selection on Microarray Data

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IEEE International Conference on Systems, Man and Cybernetics

作者： Canul-Reich, Juana Hall, Lawrence O. Goldgof, Dmitry Eschrich, Steven A. Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA H Lee Moffitt Canc Ctr & Res Inst Dept Biomed Informat Tampa FL 33612 USA

ISBN: (纸本)9781424465880

Many genes and a small number of samples are problematic characteristics of microarray datasets. We investigated the impact on classification accuracy of gene selection approaches on filtered-to-200-gene datasets. Four datasets were used with 3 filters: Student's t-test, information gain, and reliefF. We applied Iterative Feature Perturbation (IFP) and Recursive Feature Elimination (SVM-RFE) for further gene selection. Both approaches resulted in accuracy improvement when used with the t-test-filtered datasets, but not when information gain or reliefF were used. An AUC analysis of the IFP and SVM-RFE accuracy curves indicated that both methods reached the highest AUC values after t-test filtering. A statistical analysis of accuracy across the best 50 genes using the Friedman/Holm test showed that IFP and SVM-RFE were significantly more accurate more often when applied to the t-test-filtered gene sets. Surprisingly, the simple t-test, applied as a filter, results in the best overall SVM accuracy and is at least as accurate as the other, more complicated filter methods.

关键词： Feature selection IFP Information gain ReliefF t-test microarray data SVM-RFE

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Adaptive Threshold for Detecting Differentially Expressed Genes in microarray data - A Simulation Study to Investigate its Performance

Adaptive Threshold for Detecting Differentially Expressed Ge...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Fukuoka, Yutaka Inaoka, Hidenori Noshiro, Makoto Tokyo Med & Dent Univ Sch Biomed Sci Tokyo Japan Kitasato Univ Sch Allied Hlth Sci Kanagawa Japan

ISBN: (纸本)9781424441242

To detect changes in gene expression data from DNA microarrays, a fixed threshold value is used in various studies. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for genes with low expression. To address this issue, we have proposed adaptive threshold, which has different values for different expression levels. In this study, the performance of the adaptive threshold method was investigated through simulations. The sensitivity in various noise conditions was in a range between 72.7 and 100% while the specificity was better than 99% for all noise conditions. These results demonstrated the good performance of the proposed method.

关键词： DNA DNA microarrays adaptive threshold method differentially expressed genes genetics genomics microarray data molecular biophysics

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A Simple yet Effective data Integration Approach to Tree-Based microarray data Classification

A Simple yet Effective Data Integration Approach to Tree-Bas...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Liu, Lin Li, Yi Liu, Bing Li, Jiuyong Univ S Australia Sch Comp & Informat Sci Mawson Lakes SA 5095 Australia

ISBN: (纸本)9781424441242

Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different experimental devices and environments. Although microarray data is well known for its inconsistencies across labs, we demonstrate that it is possible to build consistent models using data sets from multiple labs. We report our method, experimental results and observations in the paper.

关键词： bioinformatics cellular biophysics classification models data analysis data integration decision tree decision trees gene expression levels genetics genomics microarray data pattern classification random forest tree-based classification models

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Selecting Informative Genes from microarray data by Using a Cyclic GA-based Method

Selecting Informative Genes from Microarray Data by Using a ...

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UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan Univ Teknol Malaysia Dept Software Engn Skudai 81310 Johore Malaysia

ISBN: (纸本)9781424459841

microarray data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contributes to a cancer disease. This selection process is difficult due to the availability of a small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes a cyclic method based on genetic algorithms (GA) to select a near-optimal (small) subset of informative genes that is relevant for cancer classification. The performance of the proposed method was evaluated by three benchmark microarray data sets and obtained encouraging results as compared with other experimented methods and previous related works.

关键词： component Cyclic approach Genetic algorithms Gene selection hybrid method microarray data

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Extracting Very Simple Diagnostic Rules from microarray data

Extracting Very Simple Diagnostic Rules from Microarray Data

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Wang, Lipo Chu, Feng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781424441242

We present an approach to deriving very simple classification rules from microarray data by first selecting very small gene subsets that can ensure highly accurate classification of cancers. Finding such minimum gene subsets can greatly reduce the computational load and "noise" arising from irrelevant genes. The derived simple classification rules allow for accurate diagnosis without the need for any classifiers. This work can simplify gene expression tests by including only a very small number of genes rather than thousands or tens of thousands of genes, which can significantly bring down the cost for cancer testing. These studies also call for further investigations into possible biological relationship between these small number of genes and cancer development and treatment. For example, we report the following simple, and yet 100% accurate, diagnostic rules involving only 2 genes to separate the 3 types of lymphoma patients: the patient has diffuse large B-cell lymphoma (DLBCL), if and only if the expression level of gene GENE1622X is greater than -0.75;the patient has chronic lymphocytic leukaemia (CLL), if and only if the expression level of gene GENE540X is less than -1;and the patient has follicular lymphoma (FL) otherwise, i.e., if and only if the expression level of gene GENE1622X is less than -0.75 and the expression level of gene GENE540X is greater than -1.

关键词： cancer cancer testing cancers cellular biophysics chronic lymphocytic leukaemia diagnostic rules diffuse large B-cell lymphoma follicular lymphoma gene GENE1622X gene expression tests gene subsets genetics lymphoma patients medical computing microarray data molecular biophysics noise patient diagnosis patient treatment patient treatment

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microarray Classification of Cancerous Cell Using Soft Computing Technique

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Procedia Computer Science 2015年 49卷 66-73页

作者： Prachi Vijayeeta Utsav Kar Madhurima Rana Madhabananda Das B.S.P. Mishra School of Computer Engineering KIIT University Odisha India

关键词： microarray data Support Vector Machine (SVM) K-NN(K- Nearest Neighbor) Soft computing.

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