HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.Comment: 17 pages, 7 figure

Semantic warehousing of diverse biomedical information

One of the main challenges of data warehousing within biomedical information infrastructures is to enable semantic interoperability between its various stakeholders as well as other interested parties. Promoting the adoption of worldwide accepted information standards along with common controlled terminologies is the right path to achieve that. The HL7 v3 Reference Information Model (RIM) is used to derive consistent health information standards such as laboratory, clinical health record data, problem- and goal-oriented care, public health and clinical research. In this paper we describe a RIM-based warehouse which provides (1) the means for data integration gathered from disparate and diverse data sources, (2) a mixture of XML and relational schemas and (3) a uniform abstract access and query capabilities serving both healthcare and clinical research users. Through the use of constrained standards (templates),,we facilitate semantic interoperability which would be harder to achieve if we only used generic standards in use cases with unique requirements. Such semantic warehousing also lays the groundwork for harmonized representations of data, information and knowledge, and thus enables a single infrastructure to serve analysis tools, decision support applications, clinical data exchange, and point-of-care applications. In addition, we describe the implementation of that semantic warehousing within Hypergenes, a European Commission funded project focused on Essential Hypertension, to illustrate the unique concepts and capabilities of our warehouse

Biomedical data integration - capturing similarities while preserving disparities

One of the challenges of healthcare data processing, analysis and warehousing is the integration of data gathered from disparate and diverse data sources. Promoting the adoption of worldwide accepted information standards along with common terminologies and the use of technologies derived from semantic Web representation, is a suitable path to achieve that. To that end, the HL7 V3 reference information model (RIM) has been used as the underlying information model coupled with the Web ontology language (OWL) as the semantic data integration technology. In this paper we depict a biomedical data integration process and demonstrate how it was used for integrating various data sources, containing clinical, environmental and genomic data, within Hypergenes, a European Commission funded project exploring the essential hypertension disease model

Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes

BACKGROUND: Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis. RESULTS: Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Δ and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone. CONCLUSION: These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks