Discovering relations between indirectly connected biomedical concepts

Weissenborn, Dirk; Schroeder, Michael; Tsatsaronis, George

doi:10.1186/s13326-015-0021-5

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…The negative cases in the reference set were created by randomly combining the positive cases. While this is a common approach [ 13 , 15 , 53 ], it assumes that there are no undiscovered or missing relationships. Our error analysis showed this assumption to be incorrect, with at least two of the negative cases having a therapeutic relationship in reality.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the complexity of the transformation performed by the RDF2vec tool, this method provides no insight into a possible functional mechanism. Weissenborn et al created a knowledge graph based on a very large number of predicate types extracted from the biomedical literature, to which they applied machine learning [ 15 ]. They exclusively focused on literature and did not utilize the large amounts of knowledge contained in databases.…”

Section: Introductionmentioning

confidence: 99%

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Using predicate and provenance information from a knowledge graph for drug efficacy screening

Vlietstra¹,

Vos

Sijbers

et al. 2018

J Biomed Semant

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BackgroundBiomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins.ResultsUsing random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best.ConclusionWe conclude that both predicate and provenance information provide added value for drug efficacy screening.Electronic supplementary materialThe online version of this article (10.1186/s13326-018-0189-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using predicate and provenance information from a knowledge graph for drug efficacy screening

Vlietstra¹,

Vos

Sijbers

et al. 2018

J Biomed Semant

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“…Towards advanced scientific knowledge discovery, computers have been introduced to play an ever-greater role in the scientific process with automatic hypothesis generation (HG) based on machine learning. The study of automated HG has attracted considerable attention in recent years [9], [10], [11], [12], [13], [14]. The existing HG methods form three main groups.…”

Section: Introductionmentioning

confidence: 99%

“…The second group covers methods that use a combination of advanced machine learning strategies to extract and analyze hidden connections from scientific publications. These methods include but not limited to association rules [10], [12], [16], text mining [13], [17], clustering and topic modeling [2], [18], [19], and others [14], [20], [21]. However, most of the previous studies fail to capture and utilize the dynamic evolution of the entity meaning, which can provide crucial information on inferring the future connectivity of the entities (e.g., medical terms).…”

Section: Introductionmentioning

confidence: 99%

T-PAIR: Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation

Akujuobi

Spranger

Palaniappan

et al. 2020

IEEE Trans. Knowl. Data Eng.

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In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publications. Most prior studies of this problem focused on the use of static information of terms and largely ignored the temporal dynamics of scientific term relations. Even when the dynamics were considered in a few recent studies, they learned the representations for the scientific terms, rather than focusing on the term-pair relations. Since the HG problem is to predict term-pair connections, it is not enough to know with whom the terms are connected, it is more important to know how the connections have been formed (in a dynamic process). We formulate this HG problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method named T-PAIR, utilizing both the graphical structure and node attribute to encode the temporal node-pair relationship. We demonstrate the efficiency of the proposed model on three real-world datasets, which are three graphs constructed from Pubmed papers published until 2019 in Neurology, Immunotherapy, and Virology, respectively. Evaluations were conducted on predicting future term-pair relations between millions of seen terms (in the transductive setting), as well as on the relations involving unseen terms (in the inductive setting). Experiment results and case study analyses show the effectiveness of the proposed model.

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“…Recent works use deep learning techniques to anchor a specific semantic ontology in the relevant literature [4]. A very promising application of medical literature is the discovery of new relations between concepts that may lead to breakthrough treatments [5].…”

Section: Introductionmentioning

confidence: 99%

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile

et al. 2020

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Background Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area. Results We have tested our methodology in the Revista Médica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds. Conclusions Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.

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Discovering relations between indirectly connected biomedical concepts

Cited by 17 publications

References 14 publications

Using predicate and provenance information from a knowledge graph for drug efficacy screening

Using predicate and provenance information from a knowledge graph for drug efficacy screening

T-PAIR: Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile

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