Distributed Ledger for Provenance Tracking of Artificial Intelligence Assets

High availability of data is responsible for the current trends in Artificial Intelligence (AI) and Machine Learning (ML). However, high-grade datasets are reluctantly shared between actors because of lacking trust and fear of losing control. Provenance tracing systems are a possible measure to build trust by improving transparency. Especially the tracing of AI assets along complete AI value chains bears various challenges such as trust, privacy, confidentiality, traceability, and fair remuneration. In this paper we design a graph-based provenance model for AI assets and their relations within an AI value chain. Moreover, we propose a protocol to exchange AI assets securely to selected parties. The provenance model and exchange protocol are then combined and implemented as a smart contract on a permission-less blockchain. We show how the smart contract enables the tracing of AI assets in an existing industry use case while solving all challenges. Consequently, our smart contract helps to increase traceability and transparency, encourages trust between actors and thus fosters collaboration between them

ProphNet: A generic prioritization method through propagation of information

This article has been published as part of BMC Bioinformatics Volume 15 Supplement 1, 2014: Integrated Bio-Search: Selected Works from the 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012).[Background] Prioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages. Particularly, network-based prioritization tools use a network representation for the interactions between different biological entities to identify novel indirect relationships. However, current network-based prioritization tools are strongly tailored to specific domains of interest (e.g. gene-disease prioritization) and they do not allow to consider networks with more than two types of entities (e.g. genes and diseases). Therefore, the direct application of these methods to accomplish new prioritization tasks is limited.[Results] This work presents ProphNet, a generic network-based prioritization tool that allows to integrate an arbitrary number of interrelated biological entities to accomplish any prioritization task. We tested the performance of ProphNet in comparison with leading network-based prioritization methods, namely rcNet and DomainRBF, for gene-disease and domain-disease prioritization, respectively. The results obtained by ProphNet show a significant improvement in terms of sensitivity and specificity for both tasks. We also applied ProphNet to disease-gene prioritization on Alzheimer, Diabetes Mellitus Type 2 and Breast Cancer to validate the results and identify putative candidate genes involved in these diseases.[Conclusions] ProphNet works on top of any heterogeneous network by integrating information of different types of biological entities to rank entities of a specific type according to their degree of relationship with a query set of entities of another type. Our method works by propagating information across data networks and measuring the correlation between the propagated values for a query and a target sets of entities. ProphNet is available at: http://genome2.ugr.es/prophnet webcite. A Matlab implementation of the algorithm is also available at the website.This work was part of projects P08-TIC-4299 of J. A., Sevilla and TIN2009-13489 of DGICT, Madrid. It was also supported by Plan Propio de Investigación, University of Granada

Identification of Genes with Allelic Imbalance on 6p Associated with Nasopharyngeal Carcinoma in Southern Chinese

Nasopharyngeal carcinoma (NPC) is a malignancy of epithelial origin. The etiology of NPC is complex and includes multiple genetic and environmental factors. We employed case-control analysis to study the association of chromosome 6p regions with NPC. In total, 360 subjects and 360 healthy controls were included, and 233 single nucleotide polymorphisms (SNPs) on 6p were examined. Significant single-marker associations were found for SNPs rs2267633 (p = 4.49×10−5), rs2076483 (most significant, p = 3.36×10−5), and rs29230 (p = 1.43×10−4). The highly associated genes were the gamma-amino butyric acid B receptor 1 (GABBR1), human leukocyte antigen (HLA-A), and HLA complex group 9 (HCG9). Haplotypic associations were found for haplotypes AAA (located within GABBR1, p-value  = 6.46×10−5) and TT (located within HLA-A, p = 0.0014). Further investigation of the homozygous genotype frequencies between cases and controls suggested that micro-deletion regions occur in GABBR1 and neural precursor cell expressed developmentally down-regulated 9 (NEDD9). Quantitative real-time polymerase chain reaction (qPCR) using 11 pairs of NPC biopsy samples confirmed the significant decline in GABBR1 and NEDD9 mRNA expression in the cancer tissues compared to the adjacent non-tumor tissue (p<0.05). Our study demonstrates that multiple chromosome 6p susceptibility loci contribute to the risk of NPC, possibly though GABBR1 and NEDD9 loss of function