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Motivation: A major challenge in the development of machine learning basedmethods in computational biology is that data may not be accurately labeled dueto the time and resources required for experimentally annotating properties ofproteins and DNA sequences. Standard supervised learning algorithms assumeaccurate instance-level labeling of training data. Multiple instance learningis a paradigm for handling such labeling ambiguities. However, the widely usedlarge-margin classification methods for multiple instance learning areheuristic in nature with high computational requirements. In this paper, wepresent stochastic sub-gradient optimization large margin algorithms formultiple instance classification and ranking, and provide them in a softwaresuite called pyLEMMINGS.  Results: We have tested pyLEMMINGS on a number of bioinformatics problems aswell as benchmark datasets. pyLEMMINGS has successfully been able to identifyfunctionally important segments of proteins: binding sites in Calmodulinbinding proteins, prion forming regions, and amyloid cores. pyLEMMINGS achievesstate-of-the-art performance in all these tasks, demonstrating the value ofmultiple instance learning. Furthermore, our method has shown more than100-fold improvement in terms of running time as compared to heuristicsolutions with improved accuracy over benchmark datasets.  Availability and Implementation: pyLEMMINGS python package is available fordownload at: http://faculty.pieas.edu.pk/fayyaz/software.html#pylemmings.

pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications