Data mining in bioinformatics using Weka

By Eibe Frank, Mark A. Hall, Leonard E. Trigg, G. Holmes, Ian H. Witten. 2004

937 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “We applied the different machine learning tools in the WEKA suite [35] to our data with default parameters.”
Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online (2013, methodology): “This software has been extensively used in previous research and provides accurate classification results, including in health care [13-15].”
Heterogeneous feature machines for visual recognition (2009, methodology): “To show the effectiveness of our HFM approach, we compared with the classification results using random forests [8] and Bayesian Net with the help of public software Weka [12].”
Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds (2015, neutral): “BA is Balanced Accuracy; MCC is Matthews correlation coefficient; RF is random Forest [30]; J48 is Weka [31] implementation of decision trees.”
ROC analysis: applications to the classification of biological sequences and 3D structures (2008, background): “WEKA is an open source toolkit for machine learning designed for developing and testing algorithms for data mining [48].”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, methodology): “The WEKA workbench [48] is a data mining environment that includes several machine learning algorithms including RF.”
High-Content Screening for Quantitative Cell Biology. (2016, methodology): “Data mining WEKA Collection of machine-learning algorithms for data mining tasks [81] www.”
A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. (2006, methodology): “The MDR kernel has been integrated as a filter module into an open-source and freely available data mining software package called Weka (Witten and Frank, 2000; Frank et al., 2004).”
Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. (2016, methodology): “The variable importance was ascertained using information gain (IG) criterion and ranked using the Ranker method (18).”
SHIFTX2: significantly improved protein chemical shift prediction (2011, methodology): “These were evaluated using the WEKA suite of machine learning tools (Frank 2004; Hall et al. 2009).”
The Protein Composition of Mitotic Chromosomes Determined Using Multiclassifier Combinatorial Proteomics (2010, positive): “Combination of Classiﬁers by Random Forest Analysis Combination of classiﬁers was performed using the WEKA implementation of random forest (RF) (Breiman, 2001; Frank et al., 2004; Hall et al., 2009).”
Real‐time PCR detection systems for Flavescence dorée and Bois noir phytoplasmas in grapevine: comparison with conventional PCR detection and application in diagnostics (2007, methodology): “Decision trees were generated using the weka machine learning workbench (Frank et al., 2004) and its algorithm J48, which is weka’s implementation of the C4·5 algorithm.”
A Directed Protein Interaction Network for Investigating Intracellular Signal Transduction (2011, methodology): “Therefore, we developed a naïve Bayesian learning strategy (22, 23) to add directionality to individual PPIs (Fig.”