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An improved SOM-based visualization technique for DNA microarray data analysis
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Effective and meaningful visualization techniques are quite important for multidimensional DNA microarray gene expression data analysis. Elucidating the cluster properties of these multidimensional data are often complex. Patterns, hypotheses on the relationships, and ultimately of the function of the gene can be analyzed and visualized by non-linear ...
Jagdish Chandra Patra +3 more
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DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments
Journal of the Royal Statistical Society Series A: Statistics in Society, 2007 Ziang Lu
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A Survey of Classification Techniques for Microarray Data Analysis
2011With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, transcriptomic, and proteomic domain can now be collected quickly and cheaply. One such technology is the microarray technology which allows researchers to gather information on expressions of thousands of genes all at the same time.
Wai-Ki Yip, Samir B. Amin, Cheng Li
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Performance Analysis of Microarray Data Classification using Machine Learning Techniques
International Journal of Knowledge Discovery in Bioinformatics, 2015Microarray technology of DNA permits simultaneous monitoring and determining of thousands of gene expression activation levels in a single experiment. Data mining technique such as classification is extensively used on microarray data for medical diagnosis and gene analysis.
Subhendu Kumar Pani +2 more
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Performance analysis of clustering techniques over microarray data: A case study
Physica A: Statistical Mechanics and Its Applications, 2018Abstract Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with
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, 2010 One 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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Genome-Wide Analysis of Pancreatic Cancer Using Microarray-Based Techniques
Pancreatology, 2009Microarray-based comparative genomic hybridisation (CGH) has allowed high-resolution analysis of DNA copy number alterations across the entire cancer genome. Recent advances in bioinformatics tools enable us to perform a robust and highly sensitive analysis of array CGH data and facilitate the discovery of novel cancer-related genes.We analysed a total
Tomohiko, Harada +3 more
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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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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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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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