  The abundance of predicted and mined but uncertain biological data show huge
needs for massive, efficient and scalable curation efforts. The human expertise
warranted by any successful curation enterprize is often economically
prohibitive especially for speculative end user queries that may not ultimately
bear fruit. So the challenge remains in devising a low cost engine capable of
delivering fast but tentative annotation and curation of a set of data items
that can be authoritatively validated by experts later demanding significantly
small investment. The aim thus is to make a large volume of predicted data
available for use as early as possible with an acceptable degree of confidence
in their accuracy while the curation continues. In this paper, we present a
novel approach to annotation and curation of biological database contents using
crowd computing. The technical contribution is in the identification and
management of trust of mechanical turks, and support for ad hoc declarative
queries, both of which are leveraged to support reliable analytics using noisy
predicted interactions.
