Workflow-based data analysis with KNIME

Analyze This!

KNIME Can Do More

This article has shown how you can process, visualize, and analyze data in KNIME. These examples used simple tables with numbers and text, but thanks to the free extensions provided by KNIME and other developers, much more is possible. For example, mass spectrometry and gene sequence data can be stored and processed in KNIME tables.

If you are interested in discovering other KNIME options, take a look at the Node Guide [15] and the KNIME community website [16]. A number of instructional videos explain the functions and concepts on YouTube [17], and if you get stuck, you are in good hands at the KNIME forum, where proven experts are happy to help

Infos

KNIME: https://www.knime.com
KNIME documentation: https://www.knime.com/documentation
KNIME node guide: https://www.knime.com/nodeguide
KNIME forum: https://www.knime.com/forum
D3.js framework: https://d3js.org
scikit-learn: http://scikit-learn.org/stable/index.html
R Project: http://www.r-project.org
H2O: http://www.h2o.ai
Weka: http://www.cs.waikato.ac.nz/ml/weka
LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm
Keras: http://keras.io
TensorFlow: http://www.tensorflow.org
DL4J: http://deeplearning4j.org
PMML: http://dmg.org/pmml/pmml-v4-3.html
Node Guide: https://www.knime.com/nodeguide
KNIME community: https://www.knime.com/knime-community
KNIME TV: https://www.youtube.com/user/KNIMETV

The Author

Alexander Fillbrunn is a PhD student at the Department of Bioinformatics and Information Mining at the University of Konstanz. He is particularly interested in the development of machine learning algorithms.

Martin Horn is a postdoc at the Department of Bioinformatics and Information Mining at the University of Konstanz, where he studies data analysis – of course using KNIME.

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