The article discusses the use of Weka, a machine learning workbench, in bioinformatics for various data mining tasks such as classification, regression, clustering, and feature selection. Weka provides a comprehensive set of algorithms and data preprocessing methods, complemented by graphical user interfaces for data exploration and experimental comparison. The main objectives of Weka are to assist users in extracting useful information from data and to help them identify suitable algorithms for generating accurate predictive models. The article highlights several applications of Weka in bioinformatics, including automated protein annotation, probe selection for gene-expression arrays, and cancer diagnosis. It also describes the Weka Explorer, a key interface for data loading, preprocessing, and analysis, and mentions other interfaces like Knowledge Flow and Experimenter for more process-oriented and experimental tasks. The article concludes with acknowledgments and references to supporting research.The article discusses the use of Weka, a machine learning workbench, in bioinformatics for various data mining tasks such as classification, regression, clustering, and feature selection. Weka provides a comprehensive set of algorithms and data preprocessing methods, complemented by graphical user interfaces for data exploration and experimental comparison. The main objectives of Weka are to assist users in extracting useful information from data and to help them identify suitable algorithms for generating accurate predictive models. The article highlights several applications of Weka in bioinformatics, including automated protein annotation, probe selection for gene-expression arrays, and cancer diagnosis. It also describes the Weka Explorer, a key interface for data loading, preprocessing, and analysis, and mentions other interfaces like Knowledge Flow and Experimenter for more process-oriented and experimental tasks. The article concludes with acknowledgments and references to supporting research.