scholarly journals matchprobes: a Bioconductor package for the sequence-matching of microarray probe elements

2004 ◽  
Vol 20 (10) ◽  
pp. 1651-1652 ◽  
Author(s):  
W. Huber ◽  
R. Gentleman
Keyword(s):  
Bioconductor Package ◽  
Sequence Matching ◽  
Microarray Probe

An efficient approach for sequence matching in large DNA databases

2006 ◽  
Vol 32 (1) ◽  
pp. 88-104 ◽  
Author(s):  
Jung-Im Won ◽  
Sanghyun Park ◽  
Jee-Hee Yoon ◽  
Sang-Wook Kim
Keyword(s):  
Sequence Matching ◽  
Dna Databases ◽  
Efficient Approach

scholarly journals RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data

2017 ◽  
pp. btw719
Author(s):  
Ravi D. Shankar ◽  
Sanchita Bhattacharya ◽  
Chethan Jujjavarapu ◽  
Sandra Andorf ◽  
Jeffery A. Wiser ◽  
...  
Keyword(s):  
Research Data ◽  
Bioconductor Package

scholarly journals ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data

2009 ◽  
Vol 25 (19) ◽  
pp. 2607-2608 ◽  
Author(s):  
M. Morgan ◽  
S. Anders ◽  
M. Lawrence ◽  
P. Aboyoun ◽  
H. Pages ◽  
...  
Keyword(s):  
Quality Assessment ◽  
High Throughput ◽  
Sequence Data ◽  
Bioconductor Package ◽  
Input Quality

MSstatsQC 2.0: R/Bioconductor Package for Statistical Quality Control of Mass Spectrometry-Based Proteomics Experiments

2018 ◽  
Vol 18 (2) ◽  
pp. 678-686 ◽  
Author(s):  
Eralp Dogu ◽  
Sara Mohammad Taheri ◽  
Roger Olivella ◽  
Florian Marty ◽  
Ian Lienert ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Statistical Quality Control ◽  
Bioconductor Package ◽  
Statistical Quality

scholarly journals HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1459 ◽  
Author(s):  
Lukas M. Weber ◽  
Charlotte Soneson
Keyword(s):  
Method Development ◽  
Computational Analysis ◽  
High Dimensional ◽  
Bioconductor Package ◽  
Crucial Step ◽  
New Methods ◽  
The Future ◽  
Benchmark Datasets

Benchmarking is a crucial step during computational analysis and method development. Recently, a number of new methods have been developed for analyzing high-dimensional cytometry data. However, it can be difficult for analysts and developers to find and access well-characterized benchmark datasets. Here, we present HDCytoData, a Bioconductor package providing streamlined access to several publicly available high-dimensional cytometry benchmark datasets. The package is designed to be extensible, allowing new datasets to be contributed by ourselves or other researchers in the future. Currently, the package includes a set of experimental and semi-simulated datasets, which have been used in our previous work to evaluate methods for clustering and differential analyses. Datasets are formatted into standard SummarizedExperiment and flowSet Bioconductor object formats, which include complete metadata within the objects. Access is provided through Bioconductor's ExperimentHub interface. The package is freely available from http://bioconductor.org/packages/HDCytoData.

scholarly journals Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

2010 ◽  
Vol 2 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Marc Halbrügge
Keyword(s):  
Goodness Of Fit ◽  
Model Development ◽  
Cognitive Model ◽  
Training Data ◽  
Sequence Matching ◽  
Data Set ◽  
Depth Analysis ◽  
Stocks And Flows ◽  
Matching Techniques

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) taskThis paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

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