Knowledge graphs (KGs) represent the connections and relationships between real-world entities. We propose a link prediction framework on KGs named Enrichment-Driven GrAph Reasoner (EDGAR) that infers new edges by mining entity-local rules. This approach is based on enrichment analysis, a well-established statistical method used to calculate mechanisms common to a set of differentially expressed genes. EDGAR’s inference results are inherently explainable and rankable, equipped with p-values for statistical significance of each enrichment-based rule. We demonstrate its effectiveness on a large-scale biomedical KG, ROBOKOP, focusing on drug repurposing for Alzheimer disease (AD) as a case study. Initially, we extracted 14 known drugs from the KG and identified 20 contextual biomarkers through enrichment analysis, shedding light on functional pathways relevant to the shared efficacy of drugs for AD. Subsequently, using the top 1,000 enrichment results, our enrichment-driven system identified 1,246 additional drug candidates for AD treatment. We validated the top 10 candidates using medical literature evidence. EDGAR is deployed within ROBOKOP, along with a web user interface. This is the first work to use enrichment analysis for either large graph completion or drug repurposing.

Population informatics is the systematic study of populations via secondary analysis of massive data collections about people, called the social genome. A major challenge in building the social genome is the difficulty in data integration of heterogeneous and uncoordinated data while protecting the confidentiality of the data subjects. Here, we present our work in designing a flexible computerized third party linkage platform, Secure Decoupled Linkage (SDLink), which can provide both privacy protection and accurate high quality integrated data using a hybrid human-machine data integration system. Our evaluation results show that chaffing used in combination with universe manipulation is very effective in blocking inferences during the clerical review process.

In this paper, we describe a new adaptive vector quantization (AVQ) algorithm designed for the coding of nonstationary sources. This new algorithm, generalized threshold replenishment (GTR), differs from prior AVQ algorithms in that it features an explicit, online consideration of both rate and distortion. Rate-distortion cost criteria are used in both the determination of nearest-neighbor codewords and the decision to update the codebook. Results presented indicate that, for the coding of an image sequence, (1) most AVQ algorithms achieve distortion much lower than that of nonadaptive VQ for the same rate (about 1.5 bits/pixel), and (2) the GTR algorithm achieves rate-distortion performance substantially superior to that of the prior AVQ algorithms for low-rate coding, being the only algorithm to achieve a rate below 1.0 bits/pixel.

Describes hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a differential-vector-quantization (DVQ) algorithm and features a special-purpose digital associative memory, the VAMPIRE chip, which has been fabricated in 2 /spl mu/m CMOS. The authors describe the DVQ algorithm, its adaptations for sampled NTSC composite-color video, and details of its hardware implementation. They conclude by presenting images drawn from real-time operation of the DVQ hardware.>

The authors demonstrate the use of a differential vector quantization (DVQ) architecture for the coding of digital images. An artificial neural network is used to develop entropy-biased codebooks which yield substantial data compression without entropy coding and are very robust with respect to transmission channel errors. Two methods are presented for variable bit-rate coding using the described DVQ algorithm. In the first method, both the encoder and the decoder have multiple codebooks of different sizes. In the second, variable bit-rates are achieved by using subsets of one fixed codebook. The performance of these approaches is compared, under conditions of error-free and error-prone channels. Results show that this coding technique yields pictures of excellent visual quality at moderate compression rate.>