Integrated networks have been demonstrated to be powerful tools for analysing genome-scale data. However, assessing dataset quality alone ignores dataset content. Data from different sources contain differing levels of bias and noise. While it is possible to attempt to remove bias and noise during network integration, this results in loss of data.
Given the amount of functional data currently being produced and the levels of noise in these data, computational methods, such as network-based analyses, are required to analyse them and generate new hypotheses. However as the amount of available biological data continues to grow, global network analyses become non-trivial. Further, individual research groups are interested in specific biological problems and, consequently, their network analyses are normally performed with regard to a specific question. Therefore, process-specific approaches which utilise all the available data are becoming increasingly important.
RelCID is a novel network integration technique that allows process-relevant probabilistic functional networks to be integrated without loss of data, by harnessing dataset biases. RelCID allows research groups to tailor network analyses to their specific interests. Therefore, the networks’ performance is increased and more relevant hypotheses may be produced to guide experimental studies. Further, the algorithm is extremely flexible and can be applied to any area of biology using a variety of Gold Standard data.
James, K., Wipat, A., Hallinan, J., Integration of full-coverage probabilistic functional networks with relevance to specific biological processes, Data Integration in the Life Sciences: 6th International Workshop (DILS), 2009. 5647 LNBI: p. 31-46