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BioWeka---extending the Weka framework for bioinformatics

Published: 10 February 2007 Publication History

Abstract

Summary: Given the growing amount of biological data, data mining methods have become an integral part of bioinformatics research. Unfortunately, standard data mining tools are often not sufficiently equipped for handling raw data such as e.g. amino acid sequences. One popular and freely available framework that contains many well-known data mining algorithms is the Waikato Environment for Knowledge Analysis (Weka). In the BioWeka project, we introduce various input formats for bioinformatics data and bioinformatics methods like alignments to Weka. This allows users to easily combine them with Weka's classification, clustering, validation and visualization facilities on a single platform and therefore reduces the overhead of converting data between different data formats as well as the need to write custom evaluation procedures that can deal with many different programs. We encourage users to participate in this project by adding their own components and data formats to BioWeka.
Availability: The software, documentation and tutorial are available at http://www.bioweka.org .

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  • (2014)Predicting protein relationships to human pathways through a relational learning approach based on simple sequence featuresIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2014.231873011:4(753-765)Online publication date: 1-Jul-2014
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cover image Bioinformatics
Bioinformatics  Volume 23, Issue 5
February 2007
127 pages

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Oxford University Press, Inc.

United States

Publication History

Published: 10 February 2007

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  • (2023)Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence DataComputational Intelligence and Neuroscience10.1155/2023/52361682023Online publication date: 1-Jan-2023
  • (2016)Buzzword detection in the scientific scenarioPattern Recognition Letters10.5555/2873819.287385769:C(42-48)Online publication date: 1-Jan-2016
  • (2014)Predicting protein relationships to human pathways through a relational learning approach based on simple sequence featuresIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2014.231873011:4(753-765)Online publication date: 1-Jul-2014
  • (2010)MMRF for Proteome annotation applied to human protein disease predictionProceedings of the 20th international conference on Inductive logic programming10.5555/2022735.2022748(67-75)Online publication date: 27-Jun-2010
  • (2010)Genome-wide DNA-binding specificity of PIL5, an Arabidopsis basic Helix-Loop-Helix (bHLH) transcription factorInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2010.0359024:5(588-599)Online publication date: 1-Oct-2010
  • (2009)The WEKA data mining softwareACM SIGKDD Explorations Newsletter10.1145/1656274.165627811:1(10-18)Online publication date: 16-Nov-2009
  • (2009)Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniquesIEEE Transactions on Information Technology in Biomedicine10.1109/TITB.2009.202043413:5(794-801)Online publication date: 1-Sep-2009

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