Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results

A benchmark for biomedical knowledge graph based similarity

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2020Os grafos de conhecimento biomédicos são cruciais para sustentar aplicações em grandes quantidades de dados nas ciências da vida e saúde. Uma das aplicações mais comuns dos grafos de conhecimento nas ciências da vida é o apoio à comparação de entidades no grafo por meio das suas descrições ontológicas. Estas descrições suportam o cálculo da semelhança semântica entre duas entidades, e encontrar as suas semelhanças e diferenças é uma técnica fundamental para diversas aplicações, desde a previsão de interações proteína-proteína até à descoberta de associações entre doenças e genes, a previsão da localização celular de proteínas, entre outros. Na última década, houve um esforço considerável no desenvolvimento de medidas de semelhança semântica para grafos de conhecimento biomédico mas, até agora, a investigação nessa área tem-se concentrado na comparação de conjuntos de entidades relativamente pequenos. Dada a diversa gama de aplicações para medidas de semelhança semântica, é essencial apoiar a avaliação em grande escala destas medidas. No entanto, fazê-lo não é trivial, uma vez que não há um padrão ouro para a semelhança de entidades biológicas. Uma solução possível é comparar estas medidas com outras medidas ou proxies de semelhança. As entidades biológicas podem ser comparadas através de diferentes ângulos, por exemplo, a semelhança de sequência e estrutural de duas proteínas ou as vias metabólicas afetadas por duas doenças. Estas medidas estão relacionadas com as características relevantes das entidades, portanto podem ajudar a compreender como é que as abordagens de semelhança semântica capturam a semelhança das entidades. O objetivo deste trabalho é desenvolver um benchmark, composto por data sets e métodos de avaliação automatizados. Este benchmark deve sustentar a avaliação em grande escala de medidas de semelhança semântica para entidades biológicas, com base na sua correlação com diferentes propriedades das entidades. Para atingir este objetivo, uma metodologia para o desenvolvimento de data sets de referência para semelhança semântica foi desenvolvida e aplicada a dois grafos de conhecimento: proteínas anotadas com a Gene Ontology e genes anotados com a Human Phenotype Ontology. Este benchmark explora proxies de semelhança com base na semelhança de sequência, função molecular e interações de proteínas e semelhança de genes baseada em fenótipos, e fornece cálculos de semelhança semântica com medidas representativas do estado da arte, para uma avaliação comparativa. Isto resultou num benchmark composto por uma coleção de 21 data sets de referência com tamanhos variados, cobrindo quatro espécies e diferentes níveis de anotação das entidades, e técnicas de avaliação ajustadas aos data sets.Biomedical knowledge graphs are crucial to support data intensive applications in the life sciences and healthcare. One of the most common applications of knowledge graphs in the life sciences is to support the comparison of entities in the graph through their ontological descriptions. These descriptions support the calculation of semantic similarity between two entities, and finding their similarities and differences is a cornerstone technique for several applications, ranging from prediction of protein-protein interactions to the discovering of associations between diseases and genes, the prediction of cellular localization of proteins, among others. In the last decade there has been a considerable effort in developing semantic similarity measures for biomedical knowledge graphs, but the research in this area has so far focused on the comparison of relatively small sets of entities. Given the wide range of applications for semantic similarity measures, it is essential to support the large-scale evaluation of these measures. However, this is not trivial since there is no gold standard for biological entity similarity. One possible solution is to compare these measures to other measures or proxies of similarity. Biological entities can be compared through different lenses, for instance the sequence and structural similarity of two proteins or the metabolic pathways affected by two diseases. These measures relate to relevant characteristics of the underlying entities, so they can help to understand how well semantic similarity approaches capture entity similarity. The goal of this work is to develop a benchmark for semantic similarity measures, composed of data sets and automated evaluation methods. This benchmark should support the large-scale evaluation of semantic similarity measures for biomedical entities, based on their correlation to different properties of biological entities. To achieve this goal, a methodology for the development of benchmark data sets for semantic similarity was developed and applied to two knowledge graphs: proteins annotated with the Gene Ontology and genes annotated with the Human Phenotype Ontology. This benchmark explores proxies of similarity calculated based on protein sequence similarity, protein molecular function similarity, protein-protein interactions and phenotype-based gene similarity, and provides semantic similarity computations with state-of-the-art representative measures, for a comparative evaluation of the measures. This resulted in a benchmark made up of a collection of 21 benchmark data sets with varying sizes, covering four different species at different levels of annotation completion and evaluation techniques fitted to the data sets characteristics

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2021There are still more than 1,400 Mendelian conditions whose molecular cause is un known. In addition, almost all medical conditions are somehow influenced by human genetic variation. This challenge also presents itself as an opportunity to understand the mechanisms of diseases, thus allowing the development of better mitigation strategies, finding diagnostic markers and therapeutic targets. Deciphering the link between genes and diseases is one of the most demanding tasks in biomedical research. Computational approaches for gene-disease associations prediction can greatly accelerate this process, and recent developments that explore the scientific knowledge described in ontologies have achieved good results. State-of-the-art approaches that take advantage of ontologies or knowledge graphs for these predictions are typically based on semantic similarity measures that only take into consideration hierarchical relations. New developments in the area of knowledge graphs embeddings support more powerful representations but are usually limited to a single ontology, which may be insufficient in multi-domain applications such as the prediction of gene-disease associations. This dissertation proposes a novel approach of gene-disease associations prediction by exploring both the Human Phenotype Ontology and the Gene Ontology, using knowledge graph embeddings to represent gene and disease features in a shared semantic space that covers both gene function and phenotypes. Our approach integrates different methods for building the shared semantic space, as well as multiple knowledge graph embeddings algorithms and machine learning methods. The prediction performance was evaluated on curated gene-disease associations from DisGeNET and compared to classical semantic similarity measures. Our experiments demonstrate the value of employing knowledge graph embeddings based on random walks and highlight the need for closer integration of different ontologies

SEMPER: A Web-Based Support System for Patient Self-Management

The paper discusses an eHealth project which is currently developing an interactive web-based platform that assists patients to self-manage work-related disorders and alcoholism. The focus is on motivating long-term behaviour change. This is supported by an online assessment component based on the technique of motivational interviewing and a feedback component which visualizes actual behaviour in relation to intended behaviour. Disease-specific information is provided through an information portal that utilizes lightweight ontologies (associative networks) in combination with text mining. Emotional support is provided via virtual communities. The paper discusses the design rationales underlying the approach taken and outlines some implementational aspects. The paper also briefly outlines how the effectiveness of the self-management tool will be measured based on an outcome model particularly suited for health promotion

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

International audienceThe number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures

Performance assessment of ontology matching systems for FAIR data

© The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision. Results: We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched. Conclusions: Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles

Development of a text mining approach to disease network discovery

Scientific literature is one of the major sources of knowledge for systems biology, in the form of papers, patents and other types of written reports. Text mining methods aim at automatically extracting relevant information from the literature. The hypothesis of this thesis was that biological systems could be elucidated by the development of text mining solutions that can automatically extract relevant information from documents. The first objective consisted in developing software components to recognize biomedical entities in text, which is the first step to generate a network about a biological system. To this end, a machine learning solution was developed, which can be trained for specific biological entities using an annotated dataset, obtaining high-quality results. Additionally, a rule-based solution was developed, which can be easily adapted to various types of entities. The second objective consisted in developing an automatic approach to link the recognized entities to a reference knowledge base. A solution based on the PageRank algorithm was developed in order to match the entities to the concepts that most contribute to the overall coherence. The third objective consisted in automatically extracting relations between entities, to generate knowledge graphs about biological systems. Due to the lack of annotated datasets available for this task, distant supervision was employed to train a relation classifier on a corpus of documents and a knowledge base. The applicability of this approach was demonstrated in two case studies: microRNAgene relations for cystic fibrosis, obtaining a network of 27 relations using the abstracts of 51 recently published papers; and cell-cytokine relations for tolerogenic cell therapies, obtaining a network of 647 relations from 3264 abstracts. Through a manual evaluation, the information contained in these networks was determined to be relevant. Additionally, a solution combining deep learning techniques with ontology information was developed, to take advantage of the domain knowledge provided by ontologies. This thesis contributed with several solutions that demonstrate the usefulness of text mining methods to systems biology by extracting domain-specific information from the literature. These solutions make it easier to integrate various areas of research, leading to a better understanding of biological systems