  Feature selection for predictive analytics is the problem of identifying a
minimal-size subset of features that is maximally predictive of an outcome of
interest. To apply to molecular data, feature selection algorithms need to be
scalable to tens of thousands of available features. In this paper, we propose
gOMP, a highly-scalable generalisation of the Orthogonal Matching Pursuit
feature selection algorithm to several directions: (a) different types of
outcomes, such as continuous, binary, nominal, and time-to-event, (b) different
types of predictive models (e.g., linear least squares, logistic regression),
(c) different types of predictive features (continuous, categorical), and (d)
different, statistical-based stopping criteria. We compare the proposed
algorithm against LASSO, a prototypical, widely used algorithm for
high-dimensional data. On dozens of simulated datasets, as well as, real gene
expression datasets, gOMP is on par, or outperforms LASSO for case-control
binary classification, quantified outcomes (regression), and (censored)
survival times (time-to-event) analysis. gOMP has also several theoretical
advantages that are discussed. While gOMP is based on quite simple and basic
statistical ideas, easy to implement and to generalize, we also show in an
extensive evaluation that it is also quite effective in bioinformatics analysis
settings.
