Graduated: January 1, 2008
Modeling Uncertainty in Data Integration for Improving Protein Function Assignment
n this work we describe the development and evaluation of the BioMiner system for protein functional annotation. BioMiner is the implementation of a novel uncertainty model for annotation and is based on the Uncertainty in Information Integration (UII) system, a general-purpose data integration system with extended functionality to handle uncertainty in data. The informatics contributions of our work are as follows: 1) we develop and implement a first-in-class uncertainty model for annotation and illustrate the validity of the model, 2) we show that the uncertainty model is reliable by evaluating its robustness through a principled methodology, and 3) we demonstrate that the uncertainty model performs better than existing, commonly utilized, approaches through a rigorous performance evaluation.
The application of BioMiner also contributes to the expansion of domain knowledge by accurately identifying functions for proteins of unknown function, a problem of utmost importance to biology.
Last Known Position:
Associate Director of Data Science, Auransa Inc.
Peter Tarczy-Hornoch (Chair), Eugene E. Kolker, Dan Suciu, John M. Miyamoto (GSR)