Sentence examples for multiple functional association from inspiring English sources

Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function.

Following the framework of Mostafavi et al. (2008), our approach for predicting gene function from multiple networks consists of two steps: (i) it constructs a composite network from multiple functional association networks and (ii) it predicts gene function from a single composite network.

An important step in predicting gene function is the construction of a composite network from multiple functional association networks.

The approaches closest to those presented in this article are methods for integrating multiple functional association networks into one composite network with the goal of predicting gene function from the composite network.

High throughput techniques produce multiple functional association networks.

Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks.

An advantage of the methodology presented here is that ExomeWalker quickly shows whether there are candidate genes with both predicted pathogenic variants and multiple functional associations with other genes in the same disease-gene family.

Importantly, it will show all first- and second-degree interactions with the seed genes, allowing users to quickly eyeball candidate lists to determine if there are genes with multiple functional associations with the seed genes that would reward closer inspection.

The high Xd-score for this gene set pair (0.80, the significance threshold is 0.45) points to a functional association via multiple connecting molecular interactions, which is confirmed by the visualization.

Due to the occurrence of a functional association supported by multiple evidences, we devised a weighted sum (WS) method which is a variant of naïve Bayesian integration (5).

A common framework shared by many such methods is to construct a combined functional association network from multiple networks representing different sources of data, and use this combined network as input to network-based or kernel-based learning algorithms.