This page contains nightly builds of Orange 2.7 from the code repository. These are typically stable and we recommend using them.
Full package: Snapshot of Orange with Python 2.7 and required libraries
This package is recommended to those installing Orange for the first time. It includes all required libraries (Python, PythonWin, NumPy, PyQt, PyQwt ...), though it will not change any libraries you might already have.
(Also available: Orange for Python 2.6)
Pure Orange: Snapshot of Orange for Python 2.7
Use this version if you are updating from an earlier snapshot. You can install it over your existing installation.
(Also available: Orange for Python 2.6)
Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.
In Orange Canvas the analyst connects basic computational units, called widgets, into data flow analytics schemas. Two units-widgets can be connected if they share a data type. Compared to other popular tools like Taverna, Orange widgets are high-level, integrated potentially complex tasks, but are specific enough to be used independently. Even elaborate analyses rarely consist of more than ten widgets; while tasks such as clustering and enrichment analysis could be executed with up to five widgets. While building the schema each widget is independently controlled with settings, the settings do not conceptually burden the analyst.
Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.
This is a data fusion add-on for [Orange3](http://orange.biolab.si). Add-on wraps a python library TBD for data fusion and implements a set of widgets for loading of the data, definition of data fusion schema, collective matrix factorization and exploration of latent factors.Installation
To install the add-on, run
python setup.py install
To register this add-on with Orange, but keep the code in the development directory (do not copy it to Python's site-packages directory), run
python setup.py developUsage
Run Orange from the terminal by
python -m Orange.canvas
Data fusion widgets are in the toolbox bar under Data Fusion section.
Orange Multitarget is an add-on for Orange data mining software package. It extends Orange by providing methods that allow for classification of datasets with multiple classes.
Currently supported techniques:
- Binary Relevance
- Classifier Chains and Ensemble Classifier Chains
- Clustering Trees
- Neural Networks
- Partial Least Squares
Documentation can be viewed at:
Orange NMF is an add-on for Orange data mining software package. It provides non-negative matrix factorization algorithms (NMF) through NIMFA and robust singular value decomposition (rSVD). It includes widgets that deal with missing data in input matrices, their normalizations, viewing and assessing the quality of matrix factors returned by different matrix factorization algorithms.
Documentation is found at:
Widgets were designed and implemented by Fajwel Fogel (Ecole Polytechnique ParisTech). All NMF methods call NIMFA library, implemented by Marinka Zitnik (Bioinformatics Laboratory, FRI UL). Thanks also to Doug Marsteller, Stan Young (NISS), Chris Beecher, Paul Fogel.
Orange Textable is an open-source add-on bringing advanced text-analytical functionalities to the Orange Canvas data mining software package (itself open-source). It essentially enables users to build data tables on the basis of text data, by means of a flexible and intuitive interface. Look at the following example to see it in typical action.
Orange Textable offers in particular the following features:
- text data import from keyboard, files, or urls
- support for various encodings, including Unicode
- standard preprocessing and custom recoding (based on regular expressions)
- segmentation and annotation of various text units (letters, words, etc.)
- ability to extract and exploit XML-encoded annotations
- automatic, random, or arbitrary selection of unit subsets
- unit context examination using concordance and collocation tables
- calculation of frequency and complexity measures
- recoded text data and table export
The project's homepage is http://langtech.ch/textable
Documentation is hosted at: http://orange-textable.readthedocs.org/
Orange Textable was designed and implemented by LangTech Sarl on behalf of the department of language and information sciences (SLI) at the University of Lausanne (see Credits and How to cite Orange Textable).