K R Seeja (15 results)

Condition: New. pp. 124.

Condition: New.

Taschenbuch. Condition: Neu. Computational Analysis of SAGE Data | Data Mining Approach | Seeja K. R. | Taschenbuch | 104 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659201417 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. DNA Motif Discovery | Intelligent Computing Techniques | Seeja K. R. | Taschenbuch | 124 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659392924 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In post genomic era, with the availability of the large number of genome databases the focus of research has shifted from sequencing to eliciting knowledge from these databases. Data mining is a collection of techniques for discovering previously unknown, valid and useful patterns from large databases. This book gives insight into an application of a data mining technique, called association rule mining, in analyzing Serial Analysis of Gene Expression (SAGE) data. SAGE is a sequencing technique used for measuring the expression levels of genes. Traditional association rule mining algorithms are not suitable for mining gene expression data due to its wide structure. This book contains the description of a specialized association rule mining algorithm, called GeneExpMiner, for SAGE Data analysis. It also contains an application of the association rule mining, where the algorithm is applied to SAGE data, for identifying the co-regulated signature genes. Some open problems which can be considered for further research in this area is also provided at the conclusion. This book is intended to research scholars in the area of computational biology or bioinformatics. 104 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Regulatory motifs are short patterns of nucleotides, usually 5-20 bp long, found common in the promoter region of set of co-expressed genes. Identification of these motifs gives insight into the regulatory mechanism of genes, as they control the expression or regulation of a group of genes involved in a similar cellular function. Motif discovery algorithms aim to discover these common patterns, which may present in either strand of the DNA double helix. Identification of DNA motifs is complex due to mutations, which make them weekly conserved patterns. This book describes two intelligent computing algorithms for discovering regulatory motifs. First algorithm, MotifMiner, is a table driven greedy algorithm. Even though it could identify meaningful motifs from the test dataset, due to greedy approach it may fall into local optimum. The second algorithm, AISMOTIF, is an artificial immune system based pattern discovery algorithm. The major advantage of this algorithm is its ability to generate all possible motifs in the input sequences simultaneously in reasonable time. This book is intended to research scholars in the areas of pattern matching and computational biology. 124 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K.R. SeejaThe author received B.E and M.E degrees in Computer Engineering and Ph.D.in Computer Science from Jamia Hamdard University, India. Her research areas are Data Mining and Bioinformatics. She has 15 years of teaching experien.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K.R. SeejaThe author received Ph.D. in Computer Science from Jamia Hamdard University,India. Her research interests are Data Mining, Algorithm Design and Bioinformatics. She has published many research papers in referred journals. Cu.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In post genomic era, with the availability of the large number of genome databases the focus of research has shifted from sequencing to eliciting knowledge from these databases. Data mining is a collection of techniques for discovering previously unknown, valid and useful patterns from large databases. This book gives insight into an application of a data mining technique, called association rule mining, in analyzing Serial Analysis of Gene Expression (SAGE) data. SAGE is a sequencing technique used for measuring the expression levels of genes. Traditional association rule mining algorithms are not suitable for mining gene expression data due to its wide structure. This book contains the description of a specialized association rule mining algorithm, called GeneExpMiner, for SAGE Data analysis. It also contains an application of the association rule mining, where the algorithm is applied to SAGE data, for identifying the co-regulated signature genes. Some open problems which can be considered for further research in this area is also provided at the conclusion. This book is intended to research scholars in the area of computational biology or bioinformatics.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In post genomic era, with the availability of the large number of genome databases the focus of research has shifted from sequencing to eliciting knowledge from these databases. Data mining is a collection of techniques for discovering previously unknown, valid and useful patterns from large databases. This book gives insight into an application of a data mining technique, called association rule mining, in analyzing Serial Analysis of Gene Expression (SAGE) data. SAGE is a sequencing technique used for measuring the expression levels of genes. Traditional association rule mining algorithms are not suitable for mining gene expression data due to its wide structure. This book contains the description of a specialized association rule mining algorithm, called GeneExpMiner, for SAGE Data analysis. It also contains an application of the association rule mining, where the algorithm is applied to SAGE data, for identifying the co-regulated signature genes. Some open problems which can be considered for further research in this area is also provided at the conclusion. This book is intended to research scholars in the area of computational biology or bioinformatics.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Regulatory motifs are short patterns of nucleotides, usually 5-20 bp long, found common in the promoter region of set of co-expressed genes. Identification of these motifs gives insight into the regulatory mechanism of genes, as they control the expression or regulation of a group of genes involved in a similar cellular function. Motif discovery algorithms aim to discover these common patterns, which may present in either strand of the DNA double helix. Identification of DNA motifs is complex due to mutations, which make them weekly conserved patterns. This book describes two intelligent computing algorithms for discovering regulatory motifs. First algorithm, MotifMiner, is a table driven greedy algorithm. Even though it could identify meaningful motifs from the test dataset, due to greedy approach it may fall into local optimum. The second algorithm, AISMOTIF, is an artificial immune system based pattern discovery algorithm. The major advantage of this algorithm is its ability to generate all possible motifs in the input sequences simultaneously in reasonable time. This book is intended to research scholars in the areas of pattern matching and computational biology.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Regulatory motifs are short patterns of nucleotides, usually 5-20 bp long, found common in the promoter region of set of co-expressed genes. Identification of these motifs gives insight into the regulatory mechanism of genes, as they control the expression or regulation of a group of genes involved in a similar cellular function. Motif discovery algorithms aim to discover these common patterns, which may present in either strand of the DNA double helix. Identification of DNA motifs is complex due to mutations, which make them weekly conserved patterns. This book describes two intelligent computing algorithms for discovering regulatory motifs. First algorithm, MotifMiner, is a table driven greedy algorithm. Even though it could identify meaningful motifs from the test dataset, due to greedy approach it may fall into local optimum. The second algorithm, AISMOTIF, is an artificial immune system based pattern discovery algorithm. The major advantage of this algorithm is its ability to generate all possible motifs in the input sequences simultaneously in reasonable time. This book is intended to research scholars in the areas of pattern matching and computational biology.