Sequential modeling for identifying CpG island locations in human genome

Dasgupta, Nilanjan; Lin, Simon; Carin, Lawrence

doi:10.1109/lsp.2002.806062

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Dasgupta et al [69] combined wavelet transform and Hidden Markov Models to identify the location of CpG islands in Human Genome. Another DSP approach for the same purpose was introduced by Rushdi and Tuqan [70].…”

Section: Other Studies On Genomic Sequences Using Dsp Techniquesmentioning

confidence: 99%

Digital Signal Processing in the Analysis of Genomic Sequences

Lorenzo-Ginori

Rodriguez-Fuentes

Abalo

et al. 2009

CBIO

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Digital Signal Processing (DSP) applications in Bioinformatics have received great attention in recent years, where new effective methods for genomic sequence analysis, such as the detection of coding regions, have been developed. The use of DSP principles to analyze genomic sequences requires defining an adequate representation of the nucleotide bases by numerical values, converting the nucleotide sequences into time series. Once this has been done, all the mathematical tools usually employed in DSP are used in solving tasks such as identification of protein coding DNA regions, identification of reading frames, and others. In this article we present an overview of the most relevant applications of DSP algorithms in the analysis of genomic sequences, showing the main results obtained by using these techniques, analyzing their relative advantages and drawbacks, and providing relevant examples. We finally analyze some perspectives of DSP in Bioinformatics, considering recent research results on algebraic structures of the genetic code, which suggest other new DSP applications in this field, as well as the new field of Genomic Signal Processing.

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Section: Other Studies On Genomic Sequences Using Dsp Techniquesmentioning

confidence: 99%

Digital Signal Processing in the Analysis of Genomic Sequences

Lorenzo-Ginori

Rodriguez-Fuentes

Abalo

et al. 2009

CBIO

View full text Add to dashboard Cite

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“…Dasgupta et al [18] designed models to identify the gene locations in human DNA, including the Markov model, the hidden Markov model, and a wavelet-based hidden Markov tree (HMT). In HMT processing, Dasgupta et al designed adaptive wavelets matching individual CpG islands in a DNA sequence to optimize the location identification.…”

Section: A Sequence Structure Predictionmentioning

confidence: 99%

Signal processing techniques in genomic engineering

et al. 2002

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Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.

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“…As more and more applications are exploited, DNA microarray technology has attracted tremendous interest in both scientific community and industry in recent years [5], [6], [7]. With the technique of DNA microarray, the expression of all genes in the genome can be recorded simultaneously.…”

Section: Introductionmentioning

confidence: 99%

An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data

Yuvaraj¹,

Vivekanandan²

2013

2013 International Conference on Information Communication and Embedded Systems (ICICES)

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A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. The classification of tumors was and is both a practical and theoretic necessity and requirement. DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using Nonnegative Matrix Factorization (NMF). In order to improve the performance of classification, Symmetry NMF (SymNMF) is used in this approach. Then, features are extracted from the selected genes by virtue SymNMF. As a last step, an efficient machine learning approach is used to classify the tumor samples using the extracted features. In order for a better classification, Support Vector Machine with Weighted Kernel Width (WSVM) is used in this classification approach. The performance of the proposed approach is tested using colon cancer data set and the acute leukemia data set. It is observed from the experimental results that the proposed approach provides better performance when compared with the traditional approaches.

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Sequential modeling for identifying CpG island locations in human genome

Cited by 12 publications

References 10 publications

Digital Signal Processing in the Analysis of Genomic Sequences

Digital Signal Processing in the Analysis of Genomic Sequences

Signal processing techniques in genomic engineering

An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data

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