BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

The current concept of mathematical morphology (called scalar morphology here) is extended to a matrix morphology formalism. A formal definition of matrix morphology on binary images is followed by a complete pictorial example of a nontrivial application. Binary matrix morphology is extended to gray-scale matrix morphology using a traditional development. General window transforms are expressed in terms of matrices. An application to character recognition is given.>

A simulated annealing technique for automatically training a machine vision system to recognize and locate complex objects is described. In this method, the training is used to find an optimum connectivity pattern of a fixed number of inputs that have fixed weights, rather than the usual technique of finding the optimum weights for a fixed connectivity. The recognition model uses a two-layer artificial neural network, where the first layer consists of image edge vectors in four directions. Each neuron in the second layer has a fixed number of connections that connect only to those first layer edges that are best for distinguishing the object from a confusing background. Simulated annealing is used to find the best parameters for defining edges in the first layer, as well as the pattern of connections from the first to the second layer. Weights of the connections are either plus or minus one, so that multiplications are avoided, and the system speed is considerably enhanced. In industrial applications on a low-cost parallel SIMD (single instruction multiple data) architecture, objects can be trained by an unskilled user in less than 1 min, and after training, parts can be located in about 100 ms. This method has been found to work very well on integrated circuit patterns.>

The AIS-5000 is a commercially available massively parallel processor which was designed to operate in an industrial environment. It has fine-grained parallelism with up to 1024 processing elements arranged in a single-instruction multiple-data (SIMD) architecture. The processing elements are arranged in a one-dimensional chain that, for computer vision applications, can be as wide as the image itself. The overall architecture of the system is described. Various components of the system are discussed, including details of the processing elements, data I/O (input/output) pathways and parallel memory organization. A virtual two-dimensional model for programming image-based algorithms for the system is also presented. Performance benchmarks are given for certain simple and complex functions.>