The diversity rank-score function for combining human visual perception systems.

There are many situations in which a joint decision, based on the observations or decisions of multiple individuals, is desired. The challenge is determining when a combined decision is better than each of the individual systems, along with choosing the best way to perform the combination. It has been shown that the diversity between systems plays a role in the performance of their fusion. This study involved several pairs of people, each viewing an event and reporting an observation, along with their confidence level. Each observer is treated as a visual perception system, and hence an associated scoring system is created based on the observer's confidence. A diversity rank-score function on a set of observation pairs is calculated using the notion of cognitive diversity between two scoring systems in the combinatorial fusion analysis framework. The resulting diversity rank-score function graph provides a powerful visualization tool for the diversity variation among a set of system pairs, helping to identify which system pairs are most likely to show improved performance with combination.

Combining multiple ChIP-seq peak detection systems using combinatorial fusion.

BACKGROUND: Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator. RESULTS: We define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination. CONCLUSION: Our method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.