Breeding and Genetics: Computational Issues in Genomic
... GS is based on estimating effects of HD-SNP alleles using phenotypes in a training data set and using these estimates to obtain GS-EBV for selection candidates based only on HD-SNP genotypes. Genotyping HD-SNPs for all selection candidates may not be cost effective in most livestock species. Thus, w ...
... GS is based on estimating effects of HD-SNP alleles using phenotypes in a training data set and using these estimates to obtain GS-EBV for selection candidates based only on HD-SNP genotypes. Genotyping HD-SNPs for all selection candidates may not be cost effective in most livestock species. Thus, w ...
Title: The EMBL Nucleotide Sequence Database (EMBL
... biocomputational expertise of the submitter or institution concerned. Webin is an online submission tool and consists of a set of web pages with javascript checking, where the submitter is offered extensive online help, and the use of standard submission examples to provide appropriate annotation fo ...
... biocomputational expertise of the submitter or institution concerned. Webin is an online submission tool and consists of a set of web pages with javascript checking, where the submitter is offered extensive online help, and the use of standard submission examples to provide appropriate annotation fo ...
Package `matchBox`
... The number of white balls corresponds to the top ranking features that are correctly ordered (successes), while the black balls represent the features that are not correctly ordered (failures). Finally, according to this analogy, comparing the first top 10 features from each vector will correspond t ...
... The number of white balls corresponds to the top ranking features that are correctly ordered (successes), while the black balls represent the features that are not correctly ordered (failures). Finally, according to this analogy, comparing the first top 10 features from each vector will correspond t ...
Additional file - Supplementary material
... the counts for each gene and determine if the different in counts between samples was statistically significant [16]. This is similar to the Poisson method we used for our main analysis, but with an additional parameter to account for over dispersion. Each of these methods for determining differenti ...
... the counts for each gene and determine if the different in counts between samples was statistically significant [16]. This is similar to the Poisson method we used for our main analysis, but with an additional parameter to account for over dispersion. Each of these methods for determining differenti ...
https://liberles.cst.temple.edu/public/BPO/Hermansen_et_al_2016_additional_file_1.pdf
... Get all subnodes and split into set classifications For each 3R duplication event – generate subtree with root at 3R event Compute potential 3R opportunities – (limited to 1 opportunity for each subtree) Find all Post3R-Pre4R events Determine if Post3R-Pre4R events are on both sides of 3R duplicatio ...
... Get all subnodes and split into set classifications For each 3R duplication event – generate subtree with root at 3R event Compute potential 3R opportunities – (limited to 1 opportunity for each subtree) Find all Post3R-Pre4R events Determine if Post3R-Pre4R events are on both sides of 3R duplicatio ...
Clustering approaches for temporal microarray gene expression data
... The major advantage of Shape-Based algorithms is in their ability to identify as similar two expression profiles that are shifted, inverted, or both (See Figure 3). Biologically, the shifted time would mean that one gene is regulating another. The inverted shape would mean a particular mechanism is ...
... The major advantage of Shape-Based algorithms is in their ability to identify as similar two expression profiles that are shifted, inverted, or both (See Figure 3). Biologically, the shifted time would mean that one gene is regulating another. The inverted shape would mean a particular mechanism is ...
supplementary material
... The percentages of common regulator-target pairs between different eQTL mapping methods are shown in Table 2. Overlap between methods in terms of regulator-target pairs was approximately only half of the overlap in terms of eQTL-target pairs (Table 1). For SPA and cis-mapping, 41% of all regulator-t ...
... The percentages of common regulator-target pairs between different eQTL mapping methods are shown in Table 2. Overlap between methods in terms of regulator-target pairs was approximately only half of the overlap in terms of eQTL-target pairs (Table 1). For SPA and cis-mapping, 41% of all regulator-t ...
Unsupervised learning methods for the analysis of
... • Input: Similarity matrix and a threshold parameter. • Iteratively, clusters are generated one at a time. Genes are added to an open cluster, as long as their average similarity (affinity) exceeds the threshold. Then a new cluster is started. • After termination of the assignment process, objects c ...
... • Input: Similarity matrix and a threshold parameter. • Iteratively, clusters are generated one at a time. Genes are added to an open cluster, as long as their average similarity (affinity) exceeds the threshold. Then a new cluster is started. • After termination of the assignment process, objects c ...
bioinformatics
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
Missing value estimation methods for DNA microarrays.
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
Visualization of Mappings between the Gene Ontology
... for the annotation of genes, gene products and sequences. These vocabularies are used to describe the roles and properties of genes or gene products in organisms and provide a consistent characterization of gene products in various databases. Currently, there are three independent vocabularies (or p ...
... for the annotation of genes, gene products and sequences. These vocabularies are used to describe the roles and properties of genes or gene products in organisms and provide a consistent characterization of gene products in various databases. Currently, there are three independent vocabularies (or p ...
Statistical Analysis of Microarray Data
... Simple normalization of microarray data. The difference between the raw fluorescence is a meaningless number. Computing ratios allows immediate visualization of which genes are higher in the red channel than the green channel, but logarithmic transformation of this measure on the base 2 scale result ...
... Simple normalization of microarray data. The difference between the raw fluorescence is a meaningless number. Computing ratios allows immediate visualization of which genes are higher in the red channel than the green channel, but logarithmic transformation of this measure on the base 2 scale result ...
Document
... – What are the structural features of the sequences of the sequences that you are comparing? Globular/membrane protein? – What is the level of sequence identity of the compared sequences? – Does one MDM fit my data better then the others: You can use ModelGenerator or ProtTest to compare models ...
... – What are the structural features of the sequences of the sequences that you are comparing? Globular/membrane protein? – What is the level of sequence identity of the compared sequences? – Does one MDM fit my data better then the others: You can use ModelGenerator or ProtTest to compare models ...
PPT - Bioinformatics.ca
... to detect an effect, if the effect actually exists – A power of 0.8 in a clinical trial means that the study has a 80% chance of ending up with a statistically significant treatment effect if there really was an important difference between treatments. ...
... to detect an effect, if the effect actually exists – A power of 0.8 in a clinical trial means that the study has a 80% chance of ending up with a statistically significant treatment effect if there really was an important difference between treatments. ...
A Tree of Life Based on Protein Domain Organizations
... employing arithmetic mean and harmonic mean and by taking a smaller value between dOA and dOB and confirmed that geometric mean showed the best performance among them. In this sense the employment of geometric mean is rather empirical, yet it also has some basis: because the geometric mean weighs a ...
... employing arithmetic mean and harmonic mean and by taking a smaller value between dOA and dOB and confirmed that geometric mean showed the best performance among them. In this sense the employment of geometric mean is rather empirical, yet it also has some basis: because the geometric mean weighs a ...
Unsupervised Machine Learning Techniques in Genomics
... 1. Hierarchical ClusteringHierarchical clustering algorithms partition the objects into a tree of nodes, where each node represents a cluster. Each node in a tree has zero or more child nodes, which are below it in the tree; by convention, tree grow down, not up as they do in nature. Hierarchical me ...
... 1. Hierarchical ClusteringHierarchical clustering algorithms partition the objects into a tree of nodes, where each node represents a cluster. Each node in a tree has zero or more child nodes, which are below it in the tree; by convention, tree grow down, not up as they do in nature. Hierarchical me ...
DIVERSITY VERSUS DISPARITY: EXAMPLES FROM PRESENT
... Workers investigating ammonites ecology have suggested a correlation between shell morphology and sealevel variations. If such correlations could be generalized, ammonites might then be used as paleoenvironmental markers. In practice, establishing correlations runs up against a major difficulty pose ...
... Workers investigating ammonites ecology have suggested a correlation between shell morphology and sealevel variations. If such correlations could be generalized, ammonites might then be used as paleoenvironmental markers. In practice, establishing correlations runs up against a major difficulty pose ...
Clustering Time-Course Gene
... • Nearest neighbor: Distance between two clusters is the minimum of all distances between all pairs of curves, one from each cluster • Furthest neighbor: Distance between two cluster is the maximum of all distances between all pairs of curves, one from each cluster • Average linkage: Distance betwee ...
... • Nearest neighbor: Distance between two clusters is the minimum of all distances between all pairs of curves, one from each cluster • Furthest neighbor: Distance between two cluster is the maximum of all distances between all pairs of curves, one from each cluster • Average linkage: Distance betwee ...
Missing value estimation methods for DNA microarrays
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
... Each data set was pre-processed for the evaluation by removing rows and columns containing missing expression values, yielding ‘complete’ matrices. The methods were then evaluated over each dataset as follows. Between 1 and 20% of the data were deleted at random to create test data sets. Each method ...
Use of classification trees for association studies
... can be validated by sample reuse methods [Breiman et al., 1984] or assessed with the use of χ2 test for 2 × 2 tables as described in Zhang and Singer [1999]. We adopt the χ2 test to follow the tradition in linkage and association analyses. A split is regarded as unnecessary if the χ2 tests from this ...
... can be validated by sample reuse methods [Breiman et al., 1984] or assessed with the use of χ2 test for 2 × 2 tables as described in Zhang and Singer [1999]. We adopt the χ2 test to follow the tradition in linkage and association analyses. A split is regarded as unnecessary if the χ2 tests from this ...
Analysis of Gene Expression Data Using BRB-Array Tools Richard Simon
... such data in publications are prevalent (Dupuy and Simon, 2007; Michiels et al. 2005). Valid analysis of DNA microarray experiments requires substantial statistical knowledge but statisticians with expertise in microarray methods are in short supply and not available to many laboratories. BRB-ArrayT ...
... such data in publications are prevalent (Dupuy and Simon, 2007; Michiels et al. 2005). Valid analysis of DNA microarray experiments requires substantial statistical knowledge but statisticians with expertise in microarray methods are in short supply and not available to many laboratories. BRB-ArrayT ...
QTModel-UserManual.pdf
... Contrast: enable you to perform custom hypothesis test by specifying an vector C for testing the hypothesis Cb = 0 , where b is an vector of the specified effect. The effects specified in this statement must be fixed classification effects and must appear after the “Model”. For example, suppose a fi ...
... Contrast: enable you to perform custom hypothesis test by specifying an vector C for testing the hypothesis Cb = 0 , where b is an vector of the specified effect. The effects specified in this statement must be fixed classification effects and must appear after the “Model”. For example, suppose a fi ...
Machine Learning
... Bagging decision trees, also called (Bootstrap aggregating), builds multiple decision trees by repeatedly sampling training data with replacement, and voting the trees for a consensus prediction. (e.g. house pricing) Random Forest classifier uses a number of decision trees, in order to improve the c ...
... Bagging decision trees, also called (Bootstrap aggregating), builds multiple decision trees by repeatedly sampling training data with replacement, and voting the trees for a consensus prediction. (e.g. house pricing) Random Forest classifier uses a number of decision trees, in order to improve the c ...