Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Progress and Opportunities of Foundation Models in Bioinformatics

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly adept at handling large-scale, unlabeled data, a common scenario in biological contexts due to the time-consuming and costly nature of experimentally determining labeled data. This characteristic has allowed FMs to excel and achieve notable results in various downstream validation tasks, demonstrating their ability to represent diverse biological entities effectively. Undoubtedly, FMs have ushered in a new era in computational biology, especially in the realm of deep learning. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. We delve into the specifics of the problem at hand including sequence analysis, structure prediction, function annotation, and multimodal integration, comparing the structures and advancements against traditional methods. Furthermore, the review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases. Finally, we outline potential development paths and strategies for FMs in future biological research, setting the stage for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also as a roadmap for future explorations and applications of FMs in biology.Comment: 27 pages, 3 figures, 2 table

Phenotype ontologies and cross-species analysis for translational research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings

A Knowledge Graph Framework for Dementia Research Data

Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers

Extracting phenotype-gene relations from biomedical literature using distant supervision and deep learning

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019As relações entre fenótipos humanos e genes são fundamentais para entender completamente a origem de algumas abnormalidades fenotípicas e as suas doenças associadas. A literatura biomédica é a fonte mais abrangente dessas relações. Diversas ferramentas de extração de relações têm sido propostas para identificar relações entre conceitos em texto muito heterogéneo ou não estruturado, utilizando algoritmos de supervisão distante e aprendizagem profunda. Porém, a maioria dessas ferramentas requer um corpus anotado e não há nenhum corpus disponível anotado com relações entre fenótipos humanos e genes. Este trabalho apresenta o corpus Phenotype-Gene Relations (PGR), um corpus padrão-prata de anotações de fenótipos humanos e genes e as suas relações (gerado de forma automática) e dois módulos de extração de relações usando um algoritmo de distantly supervised multi-instance learning e um algoritmo de aprendizagem profunda com ontologias biomédicas. O corpus PGR consiste em 1712 resumos de artigos, 5676 anotações de fenótipos humanos, 13835 anotações de genes e 4283 relações. Os resultados do corpus foram parcialmente avaliados por oito curadores, todos investigadores nas áreas de Biologia e Bioquímica, obtendo uma precisão de 87,01%, com um valor de concordância inter-curadores de 87,58%. As abordagens de supervisão distante (ou supervisão fraca) combinam um corpus não anotado com uma base de dados para identificar e extrair entidades do texto, reduzindo a quantidade de esforço necessário para realizar anotações manuais. A distantly supervised multi-instance learning aproveita a supervisão distante e um sparse multi-instance learning algorithm para treinar um classificador de extracção de relações, usando uma base de dados padrão-ouro de relações entre fenótipos humanos e genes. As ferramentas de aprendizagem profunda de extração de relações, para tarefas de prospeção de textos biomédicos, raramente tiram proveito dos recursos específicos existentes para cada domínio, como as ontologias biomédicas. As ontologias biomédicas desempenham um papel fundamental, fornecendo informações semânticas e de ancestralidade sobre uma entidade. Este trabalho utilizou a Human Phenotype Ontology e a Gene Ontology, para representar cada par candidato como a sequência de relações entre os seus ancestrais para cada ontologia. O corpus de teste PGR foi aplicado aos módulos de extração de relações desenvolvidos, obtendo resultados promissores, nomeadamente 55,00% (módulo de aprendizagem profunda) e 73,48% (módulo de distantly supervised multi-instance learning) na medida-F. Este corpus de teste também foi aplicado ao BioBERT, um modelo de representação de linguagem biomédica pré-treinada para prospeção de texto biomédico, obtendo 67,16% em medida-F.Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations. Several relation extraction tools have been proposed to identify relations between concepts in highly heterogeneous or unstructured text, namely using distant supervision and deep learning algorithms. However, most of these tools require an annotated corpus, and there is no corpus available annotated with human phenotype-gene relations. This work presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations (generated in a fully automated manner), and two relation extraction modules using a distantly supervised multi-instance learning algorithm, and an ontology based deep learning algorithm. The PGR corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. The corpus results were partially evaluated by eight curators, all working in the fields of Biology and Biochemistry, obtaining a precision of 87.01%, with an inter-curator agreement score of 87.58%. Distant supervision (or weak supervision) approaches combine an unlabeled corpus with a knowledge base to identify and extract entities from text, reducing the amount of manual effort necessary. Distantly supervised multi-instance learning takes advantage of distant supervision and a sparse multi-instance learning algorithm to train a relation extraction classifier, using a gold standard knowledge base of human phenotype-gene relations. Deep learning relation extraction tools, for biomedical text mining tasks, rarely take advantage of existing domain-specific resources, such as biomedical ontologies. Biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. This work used the Human Phenotype Ontology and the Gene Ontology, to represent each candidate pair as the sequence of relations between its ancestors for each ontology. The PGR test-set was applied to the developed relation extraction modules, obtaining promising results, namely 55.00% (deep learning module), and 73.48% (distantly supervised multi-instance learning module) in F-measure. This test-set was also applied to BioBERT, a pre-trained biomedical language representation model for biomedical text mining, obtaining 67.16% in F-measure

A framework for identifying genotypic information from clinical records: exploiting integrated ontology structures to transfer annotations between ICD codes and Gene Ontologies

Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) and Gene Ontologies (GO). This approach is one of the early attempts to quantify the associations among clinical terms (e.g. ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations’ relevance by comparing them with two well-known disease-gene datasets (i.e. MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature

myTea: Connecting the Web to Digital Science on the Desktop

Bioinformaticians regularly access the hundreds of databases and tools that are available to them on the Web. None of these tools communicate with each other, causing the scientist to copy results manually from a Web site into a spreadsheet or word processor. myGrids' Taverna has made it possible to create templates (workflows) that automatically run searches using these databases and tools, cutting down what previously took days of work into hours, and enabling the automated capture of experimental details. What is still missing in the capture process, however, is the details of work done on that material once it moves from the Web to the desktop: if a scientist runs a process on some data, there is nothing to record why that action was taken; it is likewise not easy to publish a record of this process back to the community on the Web. In this paper, we present a novel interaction framework, built on Semantic Web technologies, and grounded in usability design practice, in particular the Making Tea method. Through this work, we introduce a new model of practice designed specifically to (1) support the scientists' interactions with data from the Web to the desktop, (2) provide automatic annotation of process to capture what has previously been lost and (3) associate provenance services automatically with that data in order to enable meaningful interrogation of the process and controlled sharing of the results

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