Semantic Web integration of Cheminformatics resources with the SADI framework

<p>Abstract</p> <p>Background</p> <p>The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web.</p> <p>Results</p> <p>We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. We demonstrate this capability by carrying out QSAR analysis backed by a simple formal ontology to determine whether a given molecule is drug-like. Further, we discuss parameter-based control over the execution of SADI SWS. Finally, we demonstrate the value of computational resource envelopment as SADI services through service reuse and ease of integration of computational functionality into formal ontologies.</p> <p>Conclusions</p> <p>The work we present here may trigger a major paradigm shift in the distribution of computational resources in chemistry. We conclude that envelopment of chemical computational resources as SADI SWS facilitates interdisciplinary research by enabling the definition of computational problems in terms of ontologies and formal logical statements instead of cumbersome and application-specific tasks and workflows.</p

Using ontology and semantic web services to support modeling in systems biology

This thesis addresses the problem of collaboration among experimental biologists and modelers in the study of systems biology by using ontology and Semantic Web Services techniques. Modeling in systems biology is concerned with using experimental information and mathematical methods to build quantitative models across different biological scales. This requires interoperation among various knowledge sources and services. Ontology and Semantic Web Services potentially provide an infrastructure to meet this requirement. In our study, we propose an ontology-centered framework within the Semantic Web infrastructure that aims at standardizing various areas of knowledge involved in the biological modeling processes. In this framework, first we specify an ontology-based meta-model for building biological models. This meta-model supports using shared biological ontologies to annotate biological entities in the models, allows semantic queries and automatic discoveries, enables easy model reuse and composition, and serves as a basis to embed external knowledge. We also develop means of transforming biological data sources and data analysis methods into Web Services. These Web Services can then be composed together to perform parameterization in biological modeling. The knowledge of decision-making and workflow of parameterization processes are then recorded by the semantic descriptions of these Web Services, and embedded in model instances built on our proposed meta-model. We use three cases of biological modeling to evaluate our framework. By examining our ontology-centered framework in practice, we conclude that by using ontology to represent biological models and using Semantic Web Services to standardize knowledge components in modeling processes, greater capabilities of knowledge sharing, reuse and collaboration can be achieved. We also conclude that ontology-based biological models with formal semantics are essential to standardize knowledge in compliance with the Semantic Web vision

Web application for reliability analysis within civil aviation domain

Analýzy spolehlivosti jsou klíčovými složkami při hodnocení posouzení rizik během fáze návrhu v leteckém průmyslu. Analýza stromu poruch (FTA) a analýza poruchových režimů a efektů (FMEA) se běžně kombinují při analýze systému a vyhodnocování možných poruch. Kombinování metodik vyžaduje sjednocení struktury dat tak, aby byla použitelná pro všechny analytické metody zároveň. Existující aplikace poskytují nástroje samostatně, což vede k nekonzistenci dat, duplikátům a překlepům při migraci napříč aplikacemi. Tato práce si klade za cíl vytvořit rozšiřitelné řešení, které by poskytlo nástroje k provedení jedné z technik FTA a FMEA a přitom se spoléhalo na ontologický model použitelný pro obě techniky zároveň. Diplomová práce analyzuje existující řešení a ontologie a na základě těchto vstupů navrhuje nezbytné požadavky, které jsou ve spolupráci se zúčastněnými doménovými odborníky prioritizovány. Výsledné řešení implementuje aplikaci zaměřenou primárně na FTA, která nabízí definování partonomie systému, konstrukci FTA a automatický převod stromů do FMEA vzhledem k jednotnému ontologickému modelu. Aplikace je na závěr otestována doménovými odborníky na základě skutečných leteckých dat.Reliability analyses are key components in a risk assessment evaluation during the design phase in an aviation industry. Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA) are commonly combined together to review the system and to evaluate possible failures. The combination of methodologies requires a unified data usable for all the analyses. Existing applications provide the tools separately which introduces inconsistencies, duplicates and typos when the data are migrated across the applications. This thesis thus aims to create an extensible solution that would provide tools to perform one of FTA and FMEA techniques and yet rely on an ontological model usable for both. The thesis analyses existing solutions and ontologies and given these inputs proposes necessary requirements that are prioritized in cooperation with involved domain experts. The resulting solution implements an application focusing primarily on FTA which offers possibilities for system partonomy definition, FTA construction and an automatic conversion of the trees to FMEA tables given the unified ontological model. The application is finally reviewed by the domain experts on real aviation data

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.&#xd;&#xa;&#xd;&#xa;Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving&#xd;&#xa;the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Scalable Knowledge Graph Construction and Inference on Human Genome Variants

Real-world knowledge can be represented as a graph consisting of entities and relationships between the entities. The need for efficient and scalable solutions arises when dealing with vast genomic data, like RNA-sequencing. Knowledge graphs offer a powerful approach for various tasks in such large-scale genomic data, such as analysis and inference. In this work, variant-level information extracted from the RNA-sequences of vaccine-na\"ive COVID-19 patients have been represented as a unified, large knowledge graph. Variant call format (VCF) files containing the variant-level information were annotated to include further information for each variant. The data records in the annotated files were then converted to Resource Description Framework (RDF) triples. Each VCF file obtained had an associated CADD scores file that contained the raw and Phred-scaled scores for each variant. An ontology was defined for the VCF and CADD scores files. Using this ontology and the extracted information, a large, scalable knowledge graph was created. Available graph storage was then leveraged to query and create datasets for further downstream tasks. We also present a case study using the knowledge graph and perform a classification task using graph machine learning. We also draw comparisons between different Graph Neural Networks (GNNs) for the case study