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Similarity analysis of feature ranking techniques on imbalanced DNA microarray datasets
2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012DNA microarrays are a modern advancement in the analysis of genetic data. This technology allows a researcher to test samples for thousands of genes simultaneously. However, once the samples in the DNA microarrays have been tested, the researcher must then search through the data collected and identify genes important to their problem.
David J. Dittman +3 more
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DataMining Techniques for Microarray Data Analysis
2011This chapter contains sections titled: Introduction Existing Tools Improved Tools Conclusions This chapter contains sections titled ...
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Spectral Estimation Techniques for DNA Sequence and Microarray Data Analysis
Current Bioinformatics, 2007Spectral estimation techniques are widely used in modern signal processing systems. Recently, they have found important applications to the analysis of DNA data. In this paper, we review parametric and non-parametric spectral estimation methods for DNA sequence and microarray data analysis. The discrete Fourier transform (DFT) is the most commonly used
Yan, Hong, Pham, Tuan D.
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Fuzzy set-based microarray data analysis techniques for interesting block identification
2009 IEEE International Conference on Fuzzy Systems, 2009Microarrays are one of biotechnology products which enable to measure the expression level of thousands of genes simultaneously. It is sometimes crucial to identify some interesting blocks from microarray data for further investigation. Due to the massive volume of data, it is desirable to get assistance of software tools to handle this task.
Keon-Myung Lee +2 more
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2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020
Clustering is a very useful machine learning technique to find the underlying classification of unlabeled data. In computational biology, clustering techniques are extensively used to identify a group of biomolecules responsible for biological activity in animals.
Prasad Bandyopadhyay +2 more
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Clustering is a very useful machine learning technique to find the underlying classification of unlabeled data. In computational biology, clustering techniques are extensively used to identify a group of biomolecules responsible for biological activity in animals.
Prasad Bandyopadhyay +2 more
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A multi-objective feature selection and classifier ensemble technique for microarray data analysis
International Journal of Data Mining and Bioinformatics, 2018Since last few years, microarray technology has got tremendous application in many biomedical researches. Many intelligent models have been developed with different biological interpretation. This work presents a multi-objective feature selection and classifier ensemble (MOFSCE) technique for microarray data. MOFSCE works in two phases. The first phase
Rasmita Dash, Bijan Bihari Misra
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Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques
2010 Ninth International Conference on Machine Learning and Applications, 2010One of today’s most important scientific research topics is discovering the genetic links between cancers. This paper contains the results of a comparison of three different cancers (breast, colon, and lung) based on the results of feature selection techniques on a data set created from DNA micro array data consisting of samples from all three cancers.
David J. Dittman +3 more
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Journal of Computational and Theoretical Nanoscience, 2005
Microarrays are among the most powerful tools in biological research, but in order to attain its full potentialities, it is imperative to develop techniques capable to effectively exploit the huge quantity of data which they produce. In this paper two machine learning methodologies for microarray data analysis are proposed: (1) Probabilistic Principal ...
AMATO R. +9 more
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Microarrays are among the most powerful tools in biological research, but in order to attain its full potentialities, it is imperative to develop techniques capable to effectively exploit the huge quantity of data which they produce. In this paper two machine learning methodologies for microarray data analysis are proposed: (1) Probabilistic Principal ...
AMATO R. +9 more
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2006 IEEE International Conference on Systems, Man and Cybernetics, 2006
In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT)
Meng-Hsiun Tsai +3 more
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In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT)
Meng-Hsiun Tsai +3 more
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Impact of Normalization Techniques in Microarray Data Analysis
2023 IEEE Conference on Computer Applications (ICCA), 2023Lwin May Thant, Sabai Phyu
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