Alexandre Riazanov

Algorithms and semantic infrastructure for mutation impact extraction and grounding

BackgroundMutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.ResultsWe present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.ConclusionWe address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

BackgroundClinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas.ResultsA possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. In this article, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data. We have developed a prototype of a semantic querying infrastructure for the surveillance of, and research on, hospital-acquired infections.ConclusionsOur results suggest that SADI can support ad-hoc, self-service, semantic queries of relational data in a Clinical Intelligence context. The use of SADI compares favourably with approaches based on declarative semantic mappings from data schemas to ontologies, such as query rewriting and RDFizing by materialisation, because it can easily cope with situations when (i) some computation is required to turn relational data into RDF or OWL, e.g., to implement temporal reasoning, or (ii) integration with external data sources is necessary.

Deploying mutation impact text-mining software with the SADI Semantic Web Services framework

BackgroundMutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining.ResultsThis article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. SADI is a set of conventions for creating web services with semantic descriptions that facilitate automatic discovery and orchestration. We describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologically meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs.ConclusionWe have identified that SADI provides an effective way of exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases. The solutions we provide for our use cases can serve as examples to potential SADI adopters trying to solve similar integration problems.

Towards clinical intelligence with SADI semantic web services: a case study with hospital-acquired infections data

Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance and rational health care management. Ad hoc querying of clinical data is one desirable type of functionality. Since most of the data is currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas. A possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. Existing approaches to semantic querying of relational data, based on declarative semantic mappings from data schemas to ontologies, such as RDFizing and query rewriting, cannot cope with situations when some computation is required to turn relational data into RDF or OWL, e. g., to implement temporal reasoning. In this paper, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data and report preliminary progress on prototyping a semantic querying infrastructure for the surveillance of, and research on hospital-acquired infections.

PSOA2TPTP: a reference translator for interoperating PSOA RuleML with TPTP reasoners

PSOA RuleML is a recently specified rule language combining relational and object-oriented modeling. In order to provide reasoning services for PSOA RuleML, we have implemented a reference translator, PSOA2TPTP, to map knowledge bases and queries in the PSOA RuleML presentation syntax (PSOA/PS) to the popular TPTP format, supported by many first-order logic reasoners. In particular, PSOA RuleML reasoning has become available using the open-source VampirePrime reasoner, enabling query answering and entailment as well as consistency checking. The translator, currently composed of a lexer, a parser, and tree walkers, is generated by the ANTLR v3 parser generator tool from the grammars we developed. We discuss how to rewrite the original PSOA/PS grammar into an LL(1) grammar, thus demonstrating that PSOA/PS can be parsed efficiently. We also present a semantics-preserving mapping from PSOA RuleML to TPTP through a normalization and a translation phase. We wrap the translation and querying code into RESTful Web services for convenient remote access and provide a demo Web site.

Knowledge-based surveillance for preventing postoperative surgical site infection.

At least one out of every twenty people admitted to a Canadian hospital will acquire an infection. These hospital-acquired infections (HAIs) take a profound individual and system-wide toll, resulting in thousands of deaths and hundreds of millions of dollars in additional expenses each year. Surveillance for HAIs is essential to develop and evaluate prevention and control efforts. In nearly all healthcare institutions, however, surveillance for HAIs is a manual process, requiring highly trained infection control practitioners to consult multiple information systems and paper charts. The amount of effort required for discovery and integration of relevant data from multiple sources limits the current effectiveness of HAIs surveillance. In this research, we apply knowledge modeling and semantic technologies to facilitate the integration of disparate data and enable automatic reasoning with these integrated data to identify events of clinical interest. In this paper, we focus on Surgical Site Infections (SSIs), which account for a relatively large fraction of all hospital acquired infections.

From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections

We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections – Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers. The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients undergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)).

Benchmarking infrastructure for mutation text mining

BackgroundExperimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems.ResultsWe propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments.ConclusionWe have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption.

PSOA RuleML API: a tool for processing abstract and concrete syntaxes

PSOA RuleML is a rule language which introduces positional-slotted, object-applicative terms in generalized rules, permitting relation applications with optional object identifiers and positional or slotted arguments. This paper describes an open-source PSOA RuleML API, whose functionality facilitates factory-based syntactic object creation and manipulation. The API parses an XML-based concrete syntax of PSOA RuleML, creates abstract syntax objects, and uses these objects for translation into a RIF-like presentation syntax. The availability of such an API will benefit PSOA rule-based research and applications.

Valet SADI: Provisioning SADI Web Services for Semantic Querying of Relational Databases

Semantic Querying (SQ) is emerging as an attractive approach for retrieval of data from relational and other conceptually similar databases, targeting users with limited or no technical expertise. Using SQ queries can be formulated using terminologies from a specific domain, which are then either translated in real time into the equivalent SQL queries, or executed against a materialised semantic database obtained by transforming the source relational data. This approach is suitable for non-technical users who are familiar with describing a domain using the terminologies in an ontology but lack expertise in writing SQL queries over complex relational schemas. As an alternative to the existing methods, we implement the vision of SQ on relational databases by deploying Semantic Web services over one or more databases and querying them with SPARQL queries. The approach based on manual Semantic Web service writing under-utilises easily manageable declarative mappings between source data schemas and domain ontologies. In this paper we introduce the Valet SADI framework to automate the creation of SADI Semantic Web services from declarative mappings, and demonstrate that Valet SADI, together with SADI query engines, establishes semantic querying as a viable, economical and user friendly way of querying relational databases.