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Apparency and Actuality

Created: 2009-07-19 05:24:57 | Last updated: 2009-07-19 05:29:37

Credits: M. Kirsh

License: Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License

File type: Adobe PDF

Comments: 0 | Viewed: 106 times | Downloaded: 88 times

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National Centre for Text Mining (NaCTeM)

Unique name: nactem
Created: Monday 19 May 2008 16:56:09 (UTC)

1 shared item   |   0 announcements

Success-Abandonment-Classification (3)

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Retrieves data from FLOSSmole and from the Notre Dame SourceForge repository to compute project statistics based on releases, downloads and project lifespan. Project statistics are then used to classify projects according to the criteria set up in English & Schweik, but comparison criteria are parameterized so that a different set of criterion thresholds can be used to evaluate the project characteristics.

Created: 2008-02-06 | Last updated: 2008-07-02

Credits: Andrea Wiggins James Howison

ART2A Classification Workflow (1)

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This workflow loads a vector from a database and performs an ART2A classification. The ART2A classificator contains various options which are changeable within the stored workflow configuration file. There is no UI for these content available.

Created: 2008-11-17

Credits: Thomasku

Get ART2A classification result showing th... (1)

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This workflow load an ART2A classifcation results and creates a diagram showing the origin allocation within the different cluster or classes. This is usable for performing a chemical diversity analysis. The result is stored within an PDF which contains a diagram and a table of the classification result.

Created: 2008-11-17

Credits: Thomasku

Lymphoma type prediction based on microar... (7)

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Scientific value Using gene-expression patterns associated with DLBCL and FL to predict the lymphoma type of an unknown sample. Using SVM (Support Vector Machine) to classify data, and predicting the tumor types of unknown examples. Steps Querying training data from experiments stored in caArray. Preprocessing, or normalize the microarray data. Adding training and testing data into SVM service to get classification result.

Created: 2010-05-11 | Last updated: 2010-05-11

Credits: Wei Tan Ravi Stian Soiland-Reyes

Support-Vector-Machine (SVM) based data cl... (2)

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Support-Vector-Machine based data classificationSupport-Vector-Machine based data classification using genePattern SVM service, the input should be in genePattern STATML format.

Created: 2010-05-24 | Last updated: 2010-05-24

Credits: Wei Tan

Sub-Volume Probabilistic Atlas Segmentaito... (1)

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This workflow takes a raw unskull-stripped volume and a brain mask of the volume to create a tissue segmented image.This workflow is based on a novel genetic algorithm based finite mixture model and a local 3D Markov random field segmentation algorithm based on iterative conditional modes algorithm. Problem addressed by this workflow   This workflow performs tissue segmentation on the brain volumes using genetic algorithm based finite mixture model (GAMIXTURE), local 3D Markov ...

Created: 2011-04-06 | Last updated: 2011-04-06

Credits: Pipeline

date filter for ice Class Map service (NERSC) (1)

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Workflow to filter available dates with ice classification images. Service provided by NERCS. Dates are filtered using a simple regular expression

Created: 2011-11-02 | Last updated: 2011-11-04

Credits: Jorgejesus

Ice Class Map pixel analysis (NERSC) (1)

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NERSC ice classification workflow. Avaible images for a specific date will be fecthed from the WPS service, and their color table changed to allow for a user to easly identify specific areas. Statistical analysis also run using the r.stats module. Worflow uses a list of image, the list will flow thru the workflow for process resulting in mutiple outputs

Created: 2011-11-02 | Last updated: 2011-11-02

Credits: Jorgejesus

Mapping to ontologies (Gene table) (1)

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This workflow is designed to classify an input gene set to several ontologies and to identify terms, hits for which are overrepresented in the input set. The input file can be any gene or protein table.At the first step, the input table is converted into a table with Ensembl Gene IDs.This table with Ensembl Gene Ids is subjected to functional classification, which is done in parallel by the following ontologies: GO biological processes, GO cellular components, GO molecular functions, Reactome...

Created: 2013-11-13 | Last updated: 2015-04-17

Credits: geneXplain

Texture classification: Knime Image Analytics (1)

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A workflow including image pre-processing and descriptor calculation for building a texture classification model. The workflow can really be used for any kind of image classification task for which has labelled training data.There is significant scope of improvement, both on the image processing , descriptor selection and model building phases..Happy image analytics !

Created: 2015-11-10

Credits: Insilicoconsulting