Whole-genome annotation by using evidence integration in functional-linkage networks

Abstract

The advent of high-throughput biology has catalyzed a remarkable improvement in our ability to identify new genes. A large fraction of newly discovered genes have an unknown functional role, particularly when they are specific to a particular lineage or organism. These genes, currently labeled ''hypothetical,'' might support important biological cell functions and could potentially serve as targets for medical, diagnostic, or pharmacogenomic studies. An important challenge to the scientific community is to associate these newly predicted genes with a biological function that can be validated by experimental screens. In the absence of sequence or structural homology to known genes, we must rely on advanced biotechnological methods, such as DNA chips and protein-protein interaction screens as well as computational techniques to assign putative functions to these genes. In this article, we propose an effective methodology for combining biological evidence obtained in several high-throughput experimental screens and integrating this evidence in a way that provides consistent functional assignments to hypothetical genes. We use the visualization method of propagation diagrams to illustrate the flow of functional evidence that supports the functional assignments produced by the algorithm. Our results contain a number of predictions and furnish strong evidence that integration of functional information is indeed a promising direction for improving the accuracy and robustness of functional genomics.

A neural network algorithm for semi-supervised node label learning from unbalanced data

“…The complexity of this step depends on the number of iterations needed for convergence, and each iteration takes time O(|W uu |). We empirically observed that the network in average converges in few iterations, confirming the observations of Karaoz et al (2004).…”

“…This model has been used in many different areas, including content-addressable memory (Wang, 2003;Liu & Hu, 2009;Zhang & Zhang, 2005), discrete nonlinear optimization (Tsirukis et al, 1989), binary classification for GFP (Karaoz et al, 2004).…”

“…In Karaoz et al (2004) Hopfield networks have been applied to GFP; the corresponding algorithm for binary classification is named GAIN (Gene Annotation using Integrated Networks). A brief outline of GAIN is given in the following.…”

“…Methods based on the amount of functional flow through the nodes (Nabieva et al, 2005), on global graph consistency (Vazquez et al, 2003;Karaoz et al, 2004), on Markov and Gaussian Random Fields (Tsuda et al, 2005;Mostafavi et al, 2008), and recently on kernelized score functions have been applied to the predic-tion of gene functions.…”

“…Moreover, many of the described approaches may not preserve the prior knowledge coded in the initial labeling and in the pairwise similarities, and this is a relevant issue when we assume that the prior knowledge is not affected by noise. Finally, many approaches based on neural networks do not distinguish between the node labels and the values of the neuron states (Karaoz et al, 2004), thus resulting in a lower predictive capability of the network.…”