Biology subjects

Gkoutos, G. V.

Publications and source records attributed to Gkoutos, G. V..

11 recordsLinked to original sources

bioRxiv2026

PhysioMap: an ontology-grounded causal knowledge graph of human physiology

Computational physiology needs representations that connect traits across biological scales while distinguishing causal, constitutive, and mathematical dependencies. We present PhysioMap, an ontology-grounded knowledge base of contextualized physiological traits and precisely defined relation types. A versioned projection maps entailed ontology patterns to a typed causal knowledge graph that constrains quantitative structural causal models. Derivative signs provide a separate qualitative abstraction, which the PhysioMap solver uses to analyze steady-state responses in the presence of feedback. A stratified expert review across all relation types supported most sampled relations and isolated a minority for correction or further investigation. In a rare metabolic disease application, nearly all determinate predictions agreed with the HPO-derived reference before post-hoc review; after the discordant reference directions were excluded, all remaining determinate predictions agreed. Shortest signed paths produced directional errors, particularly on cases for which the PhysioMap solver did not determine a direction, indicating that its abstentions concentrated difficult cases. Abduction usually narrowed the candidate set but often did not identify a unique cause. PhysioMap therefore connects ontology-grounded physiological content to interventional prediction and abduction under incomplete quantitative knowledge. Because PhysioMap curation and the HPO-derived reference may share supporting literature, and because abduction used a closed candidate pool, these analyses do not constitute independent clinical validation.

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bioRxiv2021

Klarigi: Explanations for Semantic Groupings

BackgroundAnnotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they do not solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. ResultsWe have developed a new method, Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets. We describe the design and implementation of Klarigi, and evaluate its use in two test cases, comparing and contrasting methods and results with literature and enrichment analysis methods. ConclusionsWe demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

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bioRxiv2021

Towards Similarity-based Differential Diagnostics For Common Diseases

Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

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bioRxiv2020

Exploring binary relations for ontology extension and improved adaptation to clinical text

BackgroundThe controlled domain vocabularies provided by ontologies make them an indispensable tool for text mining. Ontologies also include semantic features in the form of taxonomy and axioms, which make annotated entities in text corpora useful for semantic analysis. Extending those semantic features may improve performance for characterisation and analytic tasks. Ontology learning techniques have previously been explored for novel ontology construction from text, though most recent approaches have focused on literature, with applications in information retrieval or human interaction tasks. We hypothesise that extension of existing ontologies using information mined from clinical narrative text may help to adapt those ontologies such that they better characterise those texts, and lead to improved classification performance. ResultsWe develop and present a framework for identifying new classes in text corpora, which can be integrated into existing ontology hierarchies. To do this, we employ the Stanford Open Information Extraction algorithm and integrate its implementation into the Komenti semantic text mining framework. To identify whether our approach leads to better characterisation of text, we present a case study, using the method to learn an adaptation to the Disease Ontology using text associated with a sample of 1,000 patient visits from the MIMIC-III critical care database. We use the adapted ontology to annotate and classify shared first diagnosis on patient visits with semantic similarity, revealing an improved performance over use of the base Disease Ontology on the set of visits the ontology was constructed from. Moreover, we show that the adapted ontology also improved performance for the same task over two additional unseen samples of 1,000 and 2,500 patient visits. ConclusionsWe report a promising new method for ontology learning and extension from text. We demonstrate that we can successfully use the method to adapt an existing ontology to a textual dataset, improving its ability to characterise the dataset, and leading to improved analytic performance, even on unseen portions of the dataset.

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bioRxiv2020

Komenti: A semantic text mining framework

SummaryKomenti is a reasoner-enabled semantic query and information extraction framework. It is the only text mining tool that enables querying inferred knowledge from biomedical ontologies. It also contains multiple novel components for vocabulary construction and context disambiguation, which can improve the power of text mining and ontology-based analysis tasks, with a view towards making full use of the semantic provision of biomedical ontologies for text characterisation and analysis. Here, we describe Komenti and its features, and present a use case wherein we automate a clinical audit, extracting medications for hypertrophic cardiomyopathy patients from text, revealing a high precision, and identifying a sub-cohort of patients with atrial fibrillation who are not anti-coagulated, and are therefore at a higher risk of stroke. Availability and ImplementationKomenti is freely available under an open source licence at http://github.com/reality/komenti. More information concerning the use-case is available in supplementary data.

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bioRxiv2020

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

BackgroundNegation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative. ResultsWe describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora. ConclusionsThe presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.

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bioRxiv2020

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

BackgroundOntologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions. ResultsWe developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combine sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. We test the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes. We design and implement a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying the minimal set of axioms that, when removed, result in a consistent and coherent set of ontologies. We applied our algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes. ConclusionsWe identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified.

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bioRxiv2019

Ontology-based prediction of cancer driver genes

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.

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bioRxiv2018

DeepPVP: phenotype-based prioritization of causative variants using deep learning

BackgroundPrioritization 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 patients phenotype.\n\nResultsWe 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.\n\nConclusionsDeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

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bioRxiv2018

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

PurposeAn increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole genome sequences.\n\nMethodsInformation that links patient phenotypes to databases of gene-phenotype associations observed in clinical research can provide useful information and improve variant prioritization for Mendelian diseases. Additionally, background knowledge about interactions between genes can be utilized to guide and restrict the selection of candidate disease modules.\n\nResultsWe developed OligoPVP, an algorithm that can be used to identify variants in oligogenic diseases and their interactions, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods.\n\nConclusionsOur results show that OligoPVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.

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bioRxiv2018

Ontology-based validation and identification of regulatory phenotypes

MotivationFunction 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.\n\nResultsWe 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. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.\n\nAvailabilityhttps://github.com/bio-ontology-research-group/phenogocon\n\nContactrobert.hoehndorf@kaust.edu.sa

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