  Motivation: A major challenge in the development of machine learning based
methods in computational biology is that data may not be accurately labeled due
to the time and resources required for experimentally annotating properties of
proteins and DNA sequences. Standard supervised learning algorithms assume
accurate instance-level labeling of training data. Multiple instance learning
is a paradigm for handling such labeling ambiguities. However, the widely used
large-margin classification methods for multiple instance learning are
heuristic in nature with high computational requirements. In this paper, we
present stochastic sub-gradient optimization large margin algorithms for
multiple instance classification and ranking, and provide them in a software
suite called pyLEMMINGS.
  Results: We have tested pyLEMMINGS on a number of bioinformatics problems as
well as benchmark datasets. pyLEMMINGS has successfully been able to identify
functionally important segments of proteins: binding sites in Calmodulin
binding proteins, prion forming regions, and amyloid cores. pyLEMMINGS achieves
state-of-the-art performance in all these tasks, demonstrating the value of
multiple instance learning. Furthermore, our method has shown more than
100-fold improvement in terms of running time as compared to heuristic
solutions with improved accuracy over benchmark datasets.
  Availability and Implementation: pyLEMMINGS python package is available for
download at: http://faculty.pieas.edu.pk/fayyaz/software.html#pylemmings.
