Search

Genetic regulatory networks have the complex task of controlling all aspects of life. Using a model of gene expression by piecewise linear differential equations we show that this process can be considered as a process of computation. This is demonstrated by showing that this model can simulate memory bounded Turing machines. The simulation is robust with respect to perturbations of the system, an important property for both analog computers and biological systems. Robustness is achieved using a condition that ensures that the model equations, that are generally chaotic, follow a predictable dynamics. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Motivation: The biological communitys reliance on computational annotations of protein function makes correct assessment of function prediction methods an issue of great importance. The fact that a large fraction of the annotations in current biological databases are based on computational methods can lead to bias in estimating the accuracy of function prediction methods. This can happen since predicting an annotation that was derived computationally in the first place is likely easier than predicting annotations that were derived experimentally, leading to over-optimistic classifier performance estimates. Results: We illustrate this phenomenon in a set of controlled experiments using a nearest neighbor classifier that uses PSI-BLAST similarity scores. Our results demonstrate that the source of Gene Ontology (GO) annotations used to assess a protein function predictor can have a highly significant influence on classifier accuracy: the average accuracy over four species and over GO terms in the biological process namespace increased from 0.72 to 0.87 when the classifier was given access to annotations that are assigned evidence codes that indicate a possible computational source, instead of experimentally determined annotations. Slightly smaller increases were observed in the other namespaces. In these comparisons the total number of annotations and their distribution across GO terms were kept the same. Conclusion: In conclusion, taking into account GO evidence codes is required for reporting accuracy statistics that do not overestimate a models performance, and is of particular importance for a fair comparison of classifiers that rely on different information sources. Contact: rogersma@cs.colostate.edu Supplementary information:Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures. Results: We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly out-performs a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel. Availability: http://noble.gs.washington.edu/proj/3dkernel Contact: noble@gs.washington.edu [ABSTRACT FROM AUTHOR]