Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays.

Technological developments in both the collection and analysis of molecular genetic data over the past few years have provided new opportunities for an improved understanding of the global response to pathogen exposure. Such developments are particularly dramatic for scientists studying the pig, where tools to measure the expression of tens of thousands of transcripts, as well as unprecedented data on the porcine genome sequence, have combined to expand our abilities to elucidate the porcine immune system. In this review, we describe these recent developments in the context of our work using primarily microarrays to explore gene expression changes during infection of pigs by Salmonella. Thus while the focus is not a comprehensive review of all possible approaches, we provide links and information on both the tools we use as well as alternatives commonly available for transcriptomic data collection and analysis of porcine immune responses. Through this review, we expect readers will gain an appreciation for the necessary steps to plan, conduct, analyze and interpret the data from transcriptomic analyses directly applicable to their research interests.

Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.

Ontologies help to identify and formally define the entities and relationships in specific domains of interest. Bio-ontologies, in particular, play a central role in the annotation, integration, analysis, and interpretation of biological data. Missing from the number of bio-ontologies is one that includes phenotypic trait information found in livestock species. As a result, the Animal Trait Ontology (ATO) project being carried out under the auspices of the USDA-National Animal Genome Research Program is aimed at the development of a standardized trait ontology for farm animals and software tools to assist the research community in collaborative creation, editing, maintenance, and use of such an ontology. The ATO is currently inclusive of cattle, pig, and chicken species, and will include other livestock species in the future. The ATO will eventually be linked to other species (e.g., human, rat, mouse) so that comparative analysis can be efficiently performed between species.

Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data.

In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT)-hierarchical groupings of attribute values-to learn compact, comprehensible and accurate classifiers from data-including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the naïve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples.

Constructive neural-network learning algorithms for pattern classification.

Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help overcome the need for ad hoc and often inappropriate choices of network topology in algorithms that search for suitable weights in a priori fixed network architectures. Several such algorithms are proposed in the literature and shown to converge to zero classification errors (under certain assumptions) on tasks that involve learning a binary to binary mapping (i.e., classification problems involving binary-valued input attributes and two output categories). We present two constructive learning algorithms MPyramid-real and MTiling-real that extend the pyramid and tiling algorithms, respectively, for learning real to M-ary mappings (i.e., classification problems involving real-valued input attributes and multiple output classes). We prove the convergence of these algorithms and empirically demonstrate their applicability to practical pattern classification problems. Additionally, we show how the incorporation of a local pruning step can eliminate several redundant neurons from MTiling-real networks.

A neural-network architecture for syntax analysis.

Artificial neural networks (ANN's), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. This paper explores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar--a prototypical symbol processing task which finds applications in programming language interpretation, syntax analysis of symbolic expressions, and high-performance compilers. The proposed architecture is assembled from ANN components for lexical analysis, stack, parsing and parse tree construction. Each of these modules takes advantage of parallel content-based pattern matching using a neural associative memory. The proposed neural-network architecture for syntax analysis provides a relatively efficient and high performance alternative to current computer systems for applications that involve parsing of LR grammars which constitute a widely used subset of deterministic context-free grammars. Comparison of quantitatively estimated performance of such a system [implemented using current CMOS very large scale integration (VLSI) technology] with that of conventional computers demonstrates the benefits of massively parallel neural-network architectures for symbol processing applications.

Is the hippocampus a Kalman filter?

Based on a large body of neurophysiological, neuroanatomical, and behavioral data, it has been suggested that the hippocampal formation serves as a spatial learning and localization system. This spatial representation is metric in nature and arises as a result of associations between sensory inputs and dead-reckoning information generated by the animal. However, despite the fact that these two information streams provide uncertain information (e.g., recognition errors, dead-reckoning drifts, etc.), the hippocampal computational models suggested to date have not explicitly addressed information fusion from erroneous sources. In this paper we develop a computational model of hippocampal spatial learning and relate its functioning to a probabilistic tool used for uncertain sensory fusion in robots: the Kalman filter. This parallel allows us to derive statistically optimal update expressions for the localization performed by our computational model.