Space-Oriented Rank-Based Data Integration

• Activity Number: 603
• Type: Invited
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Shili Lin*+
• Companies: The Ohio State University
• Keywords: Markov chain ; Omic-scaled data ; Rank aggregation ; Top-k lists ; Underlying space ; Cross entropy Monte Carlo

Abstract

One of the major challenges facing researchers studying complex biological systems is integration of data from omics platforms. For data from multiple platforms, the underlying spaces from which the top ranked elements are derived are likely to be different. Thus, taking the underlying spaces into consideration explicitly is paramount, as failure to do so would lead to inefficient use of data and might render biases and/or sub-optimal results. However, this important aspect is usually overlooked in the literature on rank-based integration methods for omic-scale data. In this talk, I will discuss a number of space oriented methods, including Markov chain based heuristic algorithms and optimization based cross entropy Monte Carlo methods for integrating ranking data. Examples will be shown to dissect the methods and to demonstrate the effects of assumptions about the underlying spaces.