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). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. RESULTS: Accuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed. CONCLUSION: We showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.

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. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations. The MEDLINE database was summarized as a complex system, consisting of nodes and edges, where the nodes refer to knowledge concepts and the edges symbolize corresponding relations. We performed an extensive statistical evaluation based on more than 25 million citations in the MEDLINE database, from 1966 until 2014. We based our analysis on node and community level in order to track temporal evolution in the network. The degree distribution of the network follows a stretched exponential distribution which prevents the creation of large hubs. Results showed that the appearance of new MeSH terms does not also imply new connections. The majority of new connections among nodes results from old MeSH descriptors. We suggest a wiring mechanism based on the theory of structural holes, according to which a novel scientific discovery is established when a connection is built among two or more previously disconnected parts of scientific knowledge. Overall, we extracted 142 different evolving communities. It is evident that new communities are constantly born, live for some time, and then die. We also provide a Web-based application that helps characterize and understand the content of extracted communities. This study clearly shows that the evolution of MEDLINE knowledge correlates with the network's structural and temporal characteristics.

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. Future use of telegenetics could increase patients' access to specialized care and help in meeting the increasing demand for genetic services.

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. Therefore, by using LBD we try to find genes or proteins that link the drugs with the reported adverse effects. These genes or proteins can be used to provide insight into the processes causing the adverse effects. Initial results show that our method has the potential to assist in explaining reported adverse drug effects.

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. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature. RESULTS: We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%). CONCLUSIONS: In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

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. METHODS: FP5 and FP6 project databases were acquired and screened by coders to identify health-related projects, which were then categorized according to the 47 divisions of the EU Health Portal (N = 2728 projects) plus an extra group of 'basic/biotech' projects (N = 1743). Country participation and coordination rates for projects were also analyzed. RESULTS: Approximately 20% of the 26 946 projects (value €29.2bn) were health-related (N = 4756. Value €6.04bn). Within the health categories, the largest expenditures were cancer (11.9%), 'other' (i.e. not mental health or cardiovascular) non-communicable diseases (9.5%) and food safety (9.4%). One hundred thirty-two countries participated in these projects. Of the 27 EU countries (and five partner countries), north-western and Nordic states acquired more projects per capita. The UK led coordination with > 20% of projects. EU-12 countries were generally under-represented for participation and coordination. CONCLUSIONS: Combining our findings with the associated literature, we comment on drivers determining distribution of participation and funds across countries and subject areas. Additionally, we discuss changes needed in the core EU projects database to provide greater transparency, data exploitation and return on investment in health research.

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). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. RESULTS: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. CONCLUSION: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.

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. METHODS: We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future. RESULTS: Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76), followed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87). CONCLUSIONS: The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.

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). BITOLA can also be used as an alternative way of searching the MEDLINE database. The system is available at http://www.mf.uni-lj.si/bitola/.

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. As a starting point, we used SemMedDB, a database of semantic relations between biomedical concepts extracted with SemRep from Medline. SemMedDB is distributed as a MySQL relational database, which has some problems when dealing with network data. We transformed and uploaded SemMedDB into the Neo4j graph database, and implemented the basic LBD discovery algorithms with the Cypher query language. We conclude that storing the data needed for semantic LBD is more natural in a graph database. Also, implementing LBD discovery algorithms is conceptually simpler with a graph query language when compared with standard SQL.

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.e., those that are likely to be semantically meaningful. For each selected co-occurring pair we derive a weighted vector representation that emphasizes the verb based functional aspects of the underlying semantics. Preliminary experiments exploring the potential value of these vectors gave us very good results. The larger goal of this project is to contribute to knowledge discovery research by mining the knowledge that is latent within the biomedical literature. It is also to provide a method capable of suggesting cross-disciplinary connections via the pairs derived from all of MEDLINE.

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. The BITOLA system can be also used as an alternative way of searching the Medline database. The system is available at http://www.mf.uni-lj.si/bitola/.

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). In this paper we propose and evaluate a methodology for link prediction of implicit connections in a network of co-occurring Medical Subject Headings (MeSH®). The proposed approach is complementary to, and may augment, existing LBD methods. Link prediction was performed using Jaccard and Adamic-Adar similarity measures. The preliminary results showed high prediction performance, with area under the ROC curve of 0.78 and 0.82 for the two similarity measures, respectively.

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. We conducted the analysis on two networks, one constructed from all MeSH descriptors and another using only major descriptors. Network reduction was performed using the Pearson's chi-square test for independence. To characterize topological properties of the network we adopted some specific measures, including diameter, average path length, clustering coefficient, and degree distribution. For the full MeSH network the average path length was 1.95 with a diameter of three edges and clustering coefficient of 0.26. The Kolmogorov-Smirnov test rejects the power law as a plausible model for degree distribution. For the major MeSH network the average path length was 2.63 edges with a diameter of seven edges and clustering coefficient of 0.15. The Kolmogorov-Smirnov test failed to reject the power law as a plausible model. The power-law exponent was 5.07. In both networks it was evident that nodes with a lower degree exhibit higher clustering than those with a higher degree. After simulated attack, where we removed 10% of nodes with the highest degrees, the giant component of each of the two networks contains about 90% of all nodes. Because of small average path length and high degree of clustering the MeSH network is small-world. A power-law distribution is not a plausible model for the degree distribution. The network is highly modular, highly resistant to targeted and random attack and with minimal dissortativity.

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. MEDLINE, the largest and most important biomedical bibliographic database, is the most common source for exploiting LBD. There are two variants of LBD, open discovery and closed discovery. With open discovery we can, for example, try to find a novel therapeutic approach for a given disease, or find new therapeutic applications for an existing drug. With closed discovery we can find an explanation for a relationship between two concepts. For example, if we already have a hypothesis that a particular drug is useful for a particular disease, with closed discovery we can identify the mechanisms through which the drug could have a therapeutic effect on the disease. We briefly describe the methodology behind LBD and then discuss in more detail currently available LBD tools; we also mention in passing some of those no longer available. Next we present several examples in which LBD has been exploited for identifying novel therapeutic approaches. In conclusion, LBD is a powerful paradigm with considerable potential to complement more traditional drug discovery methods, especially for drug target discovery and for existing drug relabeling.

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. For this reason, we attempted to facilitate discovery of novel treatments for DR by complementing the interpretation of omic results using the vast body of information existing in the published literature with the literature-based discovery (LBD) approaches. To achieve this, we collected data from transcriptomic studies performed on retinal tissue from animal models of DR, performed a meta-analysis of these datasets and identified altered genes and pathways. Using the SemBT LBD framework, we have determined which therapies could regulate perturbed pathways or that could stabilize the gene expression alterations in DR. We show that by using this approach, we not only could reidentify drugs currently in use or in clinical trials, but also could indicate novel treatment directions for ameliorating neovascularization processes in DR.

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. MEASUREMENTS AND RESULTS: Review of full-text papers in critical nodes discovered in this manner resulted in the proposal that testosterone enhances sleep by inhibiting cortisol. Using this discovery method, we posit, and could confirm as a novel hypothesis, cortisol as part of a mechanistic link elucidating the observed correlation between decreased testosterone in aging men and diminished sleep quality. CONCLUSIONS: This approach is publically available and useful not only in this manner but also to generate from the literature alternative explanatory models for observed experimental results.

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. In a process of "cooperative reciprocity" the user iteratively focuses system output, thus controlling the large number of relationships often generated in literature-based discovery systems. The underlying technology exploits SemRep semantic predications represented as a graph of interconnected nodes (predication arguments) and edges (predicates). The system suggests paths in this graph, which represent chains of relationships. The methodology is illustrated with depressive disorder and focuses on the interaction of inflammation, circadian phenomena, and the neurotransmitter norepinephrine. Insight provided may contribute to enhanced understanding of the pathophysiology, treatment, and prevention of this disorder.

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. Considerable effort is required to find, read and extract relevant information from the literature. A potential solution is to use computerized text analysis methods to extract relevant information. Our proposal enhances current methods in this regard and uses semantic relations extracted from biomedical text with the SemRep information extraction system. We describe an application that integrates microarray results with semantic relations and discuss its benefits in supporting enhanced access to the relevant literature for interpretation of results.