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The ADAM binary neural network which has been used for image analysis applications, is contructed around a central component termed a Correlation Matrix Memory (CMM). A recent reexamination of the CMM has led to development of the Advanced Uncertain Reasoning Architecture (AURA). AURA inherits many useful characteristics from ADAM, but is intended for applications requiring the manipulation of symbolic knowledge. This paper shows how the AURA architecture has been developed from ADAM and explains its method of operation. The paper also outlines the use of AURA in symbolic processing applications, and highlights some of the ways in which the AURA approach is superior to other methods.

The presence of genotyping errors can invalidate statistical tests for linkage and disease association, particularly for methods based on haplotype analysis. Becker et al. have recently proposed a simple likelihood ratio approach for detecting errors in trio genotype data. Under this approach, a SNP genotype is flagged as a potential error if the likelihood associated with the original trio genotype data increases by a multiplicative factor exceeding a user selected threshold when the SNP genotype under test is deleted. In this article we give improved error detection methods using the likelihood ratio test approach in conjunction with likelihood functions that can be efficiently computed based on a Hidden Markov Model of haplotype diversity in the population under study. Experimental results on both simulated and real datasets show that proposed methods have highly scalable running time and achieve significantly improved detection accuracy compared to previous methods.

BackgroundRecent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping.ResultsIn this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/.ConclusionsIntegration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies.

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