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This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
Molecular phylogenetics
Molecular phylogenetics
Ajay Kumar Chandra
construction of phylogenetic tree using distance based methd
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PMC Poster - phylogenetic algorithm for morphological data
PMC Poster - phylogenetic algorithm for morphological data
Yiteng Dang
Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.
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This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
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construction of phylogenetic tree using distance based methd
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Sequence Alignment in Bioinformatics
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Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.
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Contents: What does sequence mean? Examples of sequences Sequence Homology Sequence Alignment What is the use of sequence alignment? Alignment methods Tools for Sequence Alignment FASTA Format BLAST Principle of BLAST Variants of BLAST Program BLAST input BLAST output Multiple sequence alignment What is the use of multiple alignments? Multiple Alignment Method Tool for multiple alignments ClustalW input ClustalW output E (Expectation) value Demerits of progressive alignment
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SEQUENCE ANALYSIS IN BIOINFORMATICS
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Nanoporation is a highly effective method to increase permeability of intraorganelle membrane by using series of pico electric pulses. Using this technique, we can introduce specific drugs into the intraorganelle of regid cell like osreoblast and it has various application in medical science. The effect of phospholipids in respond to external pico electric fields, behaviour of water dipoles in the complex electric field landscape of the membrane interface and reorganization of water dipoles in pore formation process have been proposed in these studies. Pore characteristics such as life time, ion selectivity, size, kinetics of formation as well as number of pores are significant factors in the present study.
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Specialty gene sets, such as virulence factors and antibiotic resistance genes, are of particular interest to infectious disease researchers. Much of the information about specialty genes’ function is described in literature but unavailable as structured data in bioinformatics databases. The steadily increasing volume of literature makes it difficult to manually find relevant papers and extract assertion sentences about specialty genes. This presentation describes efforts to build and an automatic classifier for such sentences. Experiments were conducted to assess the impact of the imbalance of positive and negative examples in source documents on classification; develop a support vector machine (SVM) classifier using term frequency-inverse document frequency (TF-IDF) representation of text; and assess the marginal benefit of additional training examples on the quality of the classifier. Analysis of learning curves indicates that additional training examples will not likely improve the quality of the classifier. We discuss options for other text representation schemes to investigate in order to improve the quality of the classifier as measured by F-score.
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The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate the approach E.Coli complete genome is taken as the benchmark genome.
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Disambiguating proteins, genes,
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GeneWays http://genome6. cpmc
. columbia . edu /~ krautham / geneways /
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