  In cheminformatics, compound-target binding profiles has been a main source
of data for research. For data repositories that only provide positive
profiles, a popular assumption is that unreported profiles are all negative. In
this paper, we caution audience not to take this assumption for granted, and
present empirical evidence of its ineffectiveness from a machine learning
perspective. Our examination is based on a setting where binding profiles are
used as features to train predictive models; we show (1) prediction performance
degrades when the assumption fails and (2) explicit recovery of unreported
profiles improves prediction performance. In particular, we propose a framework
that jointly recovers profiles and learns predictive model, and show it
achieves further performance improvement. The presented study not only suggests
applying matrix recovery methods to recover unreported profiles, but also
initiates a new missing feature problem which we called Learning with Positive
and Unknown Features.
