Drug repurposing for COVID-19 via knowledge graph completion.

Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB).

Drug Repurposing for COVID-19 via Knowledge Graph Completion.

Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB).

Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective.

Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science.

Telegenetics: an Update on Availability and Use of Telemedicine in Clinical Genetics Service.

Although telegenetics as a telehealth tool for online genetic counseling was primarily initiated to improve access to genetics care in remote areas, the increasing demand for genetic services with personalized genomic medicine, shortage of clinical geneticists, and the expertise of established genetic centers make telegenetics an attractive alternative to traditional in-person genetic counseling. We review the scope of current telegenetics practice, user experience of patients and clinicians, quality of care in comparison to traditional counseling, and the advantages and disadvantages of information and communication technology in telegenetics. We found that live videoconference consultations are generally well accepted by both clients and clinicians, and these have been successfully used in several genetic counseling settings in practice.

Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery.

Objectives: Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts.

Using Literature-Based Discovery to Explain Adverse Drug Effects.

We report on our research in using literature-based discovery (LBD) to provide pharmacological and/or pharmacogenomic explanations for reported adverse drug effects. The goal of LBD is to generate novel and potentially useful hypotheses by analyzing the scientific literature and optionally some additional resources. Our assumption is that drugs have effects on some genes or proteins and that these genes or proteins are associated with the observed adverse effects.

Constructing a Graph Database for Semantic Literature-Based Discovery.

Literature-based discovery (LBD) generates discoveries, or hypotheses, by combining what is already known in the literature. Potential discoveries have the form of relations between biomedical concepts; for example, a drug may be determined to treat a disease other than the one for which it was intended. LBD views the knowledge in a domain as a network; a set of concepts along with the relations between them.

Biomedical question answering using semantic relations.

Background: The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information.

Link prediction in a MeSH co-occurrence network: preliminary results.

Literature-based discovery (LBD) refers to automatic discovery of implicit relations from the scientific literature. Co-occurrence associations between biomedical concepts are commonly used in LBD. These co-occurrences can be represented as a network that consists of a set of nodes representing concepts and a set of edges representing their relationships (or links).

Large-scale structure of a network of co-occurring MeSH terms: statistical analysis of macroscopic properties.

Concept associations can be represented by a network that consists of a set of nodes representing concepts and a set of edges representing their relationships. Complex networks exhibit some common topological features including small diameter, high degree of clustering, power-law degree distribution, and modularity. We investigated the topological properties of a network constructed from co-occurrences between MeSH descriptors in the MEDLINE database.

Integration of data from omic studies with the literature-based discovery towards identification of novel treatments for neovascularization in diabetic retinopathy.

Diabetic retinopathy (DR) is a secondary complication of diabetes associated with retinal neovascularization and represents the leading cause of blindness in the adult population in the developed world. Despite research efforts, the nature of pathogenetic processes leading to DR is still unknown, making development of novel effective treatments difficult. Advances in omic technologies now offer unprecedented insight into global molecular alterations in DR, but identification of novel treatments based on massive amounts of data generated in omic studies still represents a considerable challenge.

An analysis of subject areas and country participation for all health-related projects in the EU's FP5 and FP6 programmes.

Background: Previous analyses concerning health components of European Union (EU)-funded research have shown low project participation levels of the 12 newest member states (EU-12). Additionally, there has been a lack of subject-area analysis. In the Health Research for Europe project, we screened all projects of the EU's Framework Programmes for research FP5 and FP6 (1998-2006) to identify health research projects and describe participation by country and subject area.

Using literature-based discovery to identify novel therapeutic approaches.

We present a promising in silico paradigm called literature-based discovery (LBD) and describe its potential to identify novel pharmacologic approaches to treating diseases. The goal of LBD is to generate novel hypotheses by analyzing the vast biomedical literature. Additional knowledge resources, such as ontologies and specialized databases, are often used to supplement the published literature.

A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men.

Study Objectives: Sleep quality commonly diminishes with age, and, further, aging men often exhibit a wider range of sleep pathologies than women. We used a freely available, web-based discovery technique (Semantic MEDLINE) supported by semantic relationships to automatically extract information from MEDLINE titles and abstracts.

Design: We assumed that testosterone is associated with sleep (the A-C relationship in the paradigm) and looked for a mechanism to explain this association (B explanatory link) as a potential or partial mechanism underpinning the etiology of eroded sleep quality in aging men.

Graph-based methods for discovery browsing with semantic predications.

We present an extension to literature-based discovery that goes beyond making discoveries to a principled way of navigating through selected aspects of some biomedical domain. The method is a type of "discovery browsing" that guides the user through the research literature on a specified phenomenon. Poorly understood relationships may be explored through novel points of view, and potentially interesting relationships need not be known ahead of time.

Semantic relations for interpreting DNA microarray data.

The results from microarray experiments, in the form of lists of over- and under-expressed genes, have great potential to support progress in biomedical research. However, results are not easy to interpret. Information about the function of the genes and their relation to other genes is needed, and this information is usually present in vast amounts of biomedical literature.

A fast document classification algorithm for gene symbol disambiguation in the BITOLA literature-based discovery support system.

Gene symbol disambiguation is an important problem for biomedical text mining systems. When detecting gene symbols in MEDLINE citations one of the biggest challenges is the fact that many gene symbols also denote other, more general biomedical concepts (e.g.

Towards automatic extraction of research findings from the literature.

Most information extraction systems only process an article's title and abstract. However, a major source of research findings is an article's tables and figures. The aim of this study is to: (1) explore the efficacy of applying a hybrid information extraction system to the problem of identifying research findings in the scientific literature and (2) improve results by processing article title, abstract and table/figure text.

Using the literature-based discovery paradigm to investigate drug mechanisms.

Drug therapies are often used effectively without their underlying mechanism being completely understood. We exploit the literature-based discovery paradigm to investigate these mechanisms and propose a discovery pattern that draws on semantic predications extracted from MEDLINE citations. The use of semantic predications and the discovery pattern provides a way to uncover previously unnoticed associations between pharmacologic and bioactive substances on the one hand and bioactive substances and disorders on the other.

Exploiting semantic relations for literature-based discovery.

We propose using semantic predications to enhance literature-based discovery (LBD) systems, which currently depend exclusively on co-occurrence of words or concepts in target documents. In this paper, the predications, which are produced by the combined application of two natural language processing systems, BioMedLEE and SemRep, are coupled with an LBD system BITOLA. Initial experiments suggest this approach can uncover new associations that were not possible using previous methods.

Using literature-based discovery to identify disease candidate genes.

We present BITOLA, an interactive literature-based biomedical discovery support system. The goal of this system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database MEDLINE. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink and Human Genome Organization (HUGO).

Distilling conceptual connections from MeSH co-occurrences.

Our aim is to contribute to biomedical text extraction and mining research. In this paper we present exploratory research on the MeSH terms assigned to MEDLINE citations. We analyze MeSH based co-occurrences and identify the interesting ones, i.

Semantic relations asserting the etiology of genetic diseases.

Considerable research is being directed at extracting molecular biology information from text. Particularly challenging in this regard is to identify relations between entities, such as protein-protein interactions or molecular pathways. In this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases.

Improving literature based discovery support by genetic knowledge integration.

We present an interactive literature based biomedical discovery support system (BITOLA). The goal of the system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database Medline. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink, HUGO and OMIM.