title: Drug activity prediction using multiple-instance learning via joint instance and feature selection.
abstract: BACKGROUND
  In drug discovery and development, it is crucial to determine which conformers (instances) of a given molecule are responsible for its observed biological activity and at the same time to recognize the most representative subset of features (molecular descriptors). Due to experimental difficulty in obtaining the bioactive conformers, computational approaches such as machine learning techniques are much needed. Multiple Instance Learning (MIL) is a machine learning method capable of tackling this type of problem. In the MIL framework, each instance is represented as a feature vector, which usually resides in a high-dimensional feature space. The high dimensionality may provide significant information for learning tasks, but at the same time it may also include a large number of irrelevant or redundant features that might negatively affect learning performance. Reducing the dimensionality of data will hence facilitate the classification task and improve the interpretability of the model.
  
  
  RESULTS
  In this work we propose a novel approach, named multiple instance learning via joint instance and feature selection. The iterative joint instance and feature selection is achieved using an instance-based feature mapping and 1-norm regularized optimization. The proposed approach was tested on four biological activity datasets.
  
  
  CONCLUSIONS
  The empirical results demonstrate that the selected instances (prototype conformers) and features (pharmacophore fingerprints) have competitive discriminative power and the convergence of the selection process is also fast.
authors.forename: Zhendong
authors.initials: Z
authors.lastname: Zhao
authors.forename: Gang
authors.initials: G
authors.lastname: Fu
authors.forename: Sheng
authors.initials: S
authors.lastname: Liu
authors.forename: Khaled M
authors.initials: KM
authors.lastname: Elokely
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Doerksen
authors.forename: Yixin
authors.initials: Y
authors.lastname: Chen
authors.forename: Dawn E
authors.initials: DE
authors.lastname: Wilkins
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055808:Drug Discovery; D006801:Humans; D021621:Imaging, Three-Dimensional; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation
chemical_list: D008024:Ligands