MOTIVATION: Knowledge graphs and structural causal models have each proven valuable for organizing biomedical knowledge and estimating causal effects, but remain largely disconnected: knowledge graphs encode qualitative relationships focusing on facts and deductive reasoning without formal probabilistic semantics, while causal models lack integration with background knowledge in knowledge graphs and have no access to the deductive reasoning capabilities that knowledge graphs provide. RESULTS: To bridge this gap, we introduce a novel formulation of Causal Knowledge Graphs (CKGs) which extend knowledge graphs with formal causal semantics, preserving their deductive capabilities while enabling principled causal inference. CKGs support deconfounding via explicitly marked causal edges and facilitate hypothesis formulation aligned with both encoded and entailed background knowledge. We constructed a Drug-Disease CKG (DD-CKG) integrating disease progression pathways, drug indications, side-effects, and hierarchical disease classification to enable automated large-scale mediation analysis. Applied to UK Biobank and MIMIC-IV cohorts, we tested whether drugs mediate effects between indications and downstream disease progression, adjusting for confounders inferred from the DD-CKG. Our approach successfully reproduced known adverse drug reactions with high precision while identifying previously undocumented significant candidate adverse effects. Further validation through side effect similarity analysis demonstrated that combining our predicted drug effects with established databases significantly improves the prediction of shared drug indications, supporting the clinical relevance of our novel findings. These results demonstrate that our methodology provides a generalizable, knowledge-driven framework for scalable causal inference. AVAILABILITY AND IMPLEMENTATION: The data is available through https://github.com/bio-ontology-research-group/Mediation-Analysis-using-Causal-Knowledge-Graph.

MOTIVATION: Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks. Such causal relations can facilitate the identification of potential disease precursors and candidates for drug re-purposing. However, computational methods often lack access to these causal relations. Few approaches have been developed to automatically extract causal relationships between diseases from unstructured text, but they are often only focused on a small number of diseases, lack validation of the extracted causal relations, or do not make their data available. RESULTS: We automatically mined statements asserting a causal relation between diseases from the scientific literature by leveraging lexical patterns. Following automated mining of causal relations, we mapped the diseases to the International Classification of Diseases (ICD) identifiers to allow the direct application to clinical data. We provide quantitative and qualitative measures to evaluate the mined causal relations and compare to UK Biobank diagnosis data as a completely independent data source. The validated causal associations were used to create a directed acyclic graph that can be used by causal inference frameworks. We demonstrate the utility of our causal network by performing causal inference using the do-calculus, using relations within the graph to construct and improve polygenic risk scores, and disentangle the pleiotropic effects of variants. AVAILABILITY AND IMPLEMENTATION: The data are available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases.

Ontologies are fundamental tools for the organisation and analysis of biomedical data. One of their roles is as controlled domain vocabularies, providing standardised language and categorisation for relevant domain concepts. As such, ontologies frequently include a wealth of natural language metadata including labels and definitions. Since these metadata are usually created by humans, there exists the possibility that conscious and unconscious biases may be reflected in them. Moreover, humans and computers engage directly with these metadata during the course of scientific practice, and therefore any biases or idiosyncrasies may influence work involving the use of these concepts. Previous work has exposed the possibility of bias in ontological representations of disease domains, however there have been no methods developed for automatic or semiautomatic guidance towards bias in ontology metadata. In this article, we develop an approach to explore sentiment analysis as a potential indicator of bias in ontology concept definitions. We evaluate its use on pairs of disease classes from MESH and Human Disease Ontology (DO), comparing and contrasting sentiment scores between them. We use these examples to identify and evaluate a number of outlying examples, relating them to existing literature. We discuss how our approach could be used to guide ontology developers towards outlying and potentially biased language, forming a tool that could be used to evaluate and improve normalisation of ontology metadata. We also discuss the applicability and appropriateness of general-purpose sentiment analysis applied to biomedical texts, and potential influences of bias on computational analysis, in the context of our results.

BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. RESULTS: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp . CONCLUSIONS: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Motivation: Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations. Results: We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. We also apply our method to the rule-based prediction of regulatory phenotypes from functions and demonstrate that we can predict these phenotypes with Fmax of up to 0.647. Availability and implementation: https://github.com/bio-ontology-research-group/phenogocon.

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

A large number of biomedical resources have been developed to represent the functions of biological entities, and these resources are widely used for data integration and analysis. Expressing functions in biomedical ontologies currently uses formal representation patterns that renders basic reasoning tasks to fall in complexity classes beyond polynomial time, thereby limiting the potential of using knowledge-based methods for data integration, querying or quality control. Here, we propose an alternative representation pattern for expressing knowledge about biological functions, together with a biological and ontological justification, which can be expressed using the description logic EL++ and implemented using the OWL 2 EL profile. To demonstrate the utility of our account of biological functions, we apply it to all proteins contained in the SwissProt database and evaluate its utility with respect to answering complex queries as well with respect to the classification and query times.

Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.

BACKGROUND: Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. RESULTS: We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net . CONCLUSIONS: Aber-OWL provides a framework for automatically accessing information that is annotated with ontologies or contains terms used to label classes in ontologies. When using Aber-OWL, access to ontologies and data annotated with them is not merely based on class names or identifiers but rather on the knowledge the ontologies contain and the inferences that can be drawn from it.

MOTIVATION: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. RESULTS: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. AVAILABILITY AND IMPLEMENTATION: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com.

MOTIVATION: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies. RESULTS: We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses. AVAILABILITY AND IMPLEMENTATION: The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.

BACKGROUND: Ontologies are widely used to represent knowledge in biomedicine. Systematic approaches for detecting errors and disagreements are needed for large ontologies with hundreds or thousands of terms and semantic relationships. A recent approach of defining terms using logical definitions is now increasingly being adopted as a method for quality control as well as for facilitating interoperability and data integration. RESULTS: We show how automated reasoning over logical definitions of ontology terms can be used to improve ontology structure. We provide the Java software package GULO (Getting an Understanding of LOgical definitions), which allows fast and easy evaluation for any kind of logically decomposed ontology by generating a composite OWL ontology from appropriate subsets of the referenced ontologies and comparing the inferred relationships with the relationships asserted in the target ontology. As a case study we show how to use GULO to evaluate the logical definitions that have been developed for the Mammalian Phenotype Ontology (MPO). CONCLUSIONS: Logical definitions of terms from biomedical ontologies represent an important resource for error and disagreement detection. GULO gives ontology curators a fast and simple tool for validation of their work.

Following the technological advances that have enabled genome-wide analysis in most model organisms over the last ten years, there has been unprecedented growth in genome and post-genomic sciences with concomitant generation of an exponentially increasing volume of data. As a result numerous resources have been created to store and archive the data and biological materials produced, which are of substantial value to the whole community. Sustained access facilitating re-use of this primary data is vital, not only for validation, but for re-analysis, testing of new hypotheses and developing new technologies/platforms. A common challenge for most data resources and biological repositories today is finding financial support for maintenance and development so as to best serve the scientific community. In this manuscript we examine the problems that currently confront the data and resource infrastructure underlying the biomedical sciences. We discuss the financial sustainability issues and potential business models that could be adopted by biological resources and consider long term data preservation issues within the context of mouse functional genomics efforts in Europe.

In recent years the European Commission has funded an increasing number of functional genomics projects aimed at using the mouse as a model of human disease. Many of these projects are producing large data volumes. A recently funded programme, CASIMIR (coordination and sustainability of international mouse informatics resources) aims to make recommendations on the most efficient way to integrate these datasets. In summer 2007 CASIMIR carried out a questionnaire survey of relevant EC-funded projects to determine their current use of data integration technologies and standards. This report describes the consortiumpsilas aims, its achievements so far, the results of the survey and initial conclusions deriving from it.

A growing array of biotechnologies is being used to study the genetics of complex biomolecular traits in laboratory mice as models for human disease. Combined analysis of these datasets provides much of the power of the approach of functional genomics but this depends on the ability of databases to exchange data with each other and with analytical software. In the light of these challenges the European Commission has funded a coordination action, CASIMIR, to make recommendations on how this need might be fulfilled. We here report on two pilot projects and distill preliminary recommendations.

The Mouse Resource Browser MRB (http://bioit.fleming.gr/mrb) is an easy-to-use database for searching and retrieving mouse resource information. Currently, MRB hosts a list of 200 mouse resources which are divided in 33 different categories. Apart from core information such as URL(s), contact information and free text descriptions of domain content, MRB holds valuable technical information for each resource; server technology used, relational database management system(s) utilized, programming language(s) of implementation, schema descriptive documents or actual database dumps and most importantly information about the integration and interoperability services provided. MRB also hosts an index of ontologies - both OBO and non-OBO - and minimum information standards for biological investigations. Moreover, MRB has implemented the maturity scheme developed by the CASIMIR consortium and attempts to assess resources accordingly. Ultimately MRBpsilas goal is to provide a complete registry of mouse resources and their programmatic accessibility methods.

The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.