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Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

ABSTRACT: BACKGROUND:Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. OBJECTIVE:To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. METHODS:We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. RESULTS:Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. CONCLUSIONS:We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

SUBMITTER: Bakal G

PROVIDER: S-EPMC6070294 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

Bakal Gokhan G Talari Preetham P Kakani Elijah V EV Kavuluru Ramakanth R

Journal of biomedical informatics 20180512

<h4>Background</h4>Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine lea ...[more]

PMID: 29763706

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Project description:BackgroundThe 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.ResultsWe 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%).ConclusionsIn 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.

Project description:BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.ResultsWe benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.ConclusionWe extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

Project description:MotivationThe majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited.ResultsTo generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e. text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e. from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications.Availability and implementationWe make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain. We aim to understand how to incorporate multimodal data into biomedical KG embeddings, and analyze the resulting performance in comparison with traditional methods. We propose a modular framework for learning embeddings in KGs with entity attributes, that allows encoding attribute data of different modalities while also supporting entities with missing attributes. We additionally propose an efficient pretraining strategy for reducing the required training runtime. We train models using a biomedical KG containing approximately 2 million triples, and evaluate the performance of the resulting entity embeddings on the tasks of link prediction, and drug-protein interaction prediction, comparing against methods that do not take attribute data into account. In the standard link prediction evaluation, the proposed method results in competitive, yet lower performance than baselines that do not use attribute data. When evaluated in the task of drug-protein interaction prediction, the method compares favorably with the baselines. Further analyses show that incorporating attribute data does outperform baselines over entities below a certain node degree, comprising approximately 75% of the diseases in the graph. We also observe that optimizing attribute encoders is a challenging task that increases optimization costs. Our proposed pretraining strategy yields significantly higher performance while reducing the required training runtime. BioBLP allows to investigate different ways of incorporating multimodal biomedical data for learning representations in KGs. With a particular implementation, we find that incorporating attribute data does not consistently outperform baselines, but improvements are obtained on a comparatively large subset of entities below a specific node-degree. Our results indicate a potential for improved performance in scientific discovery tasks where understudied areas of the KG would benefit from link prediction methods.

Project description:BackgroundSemantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity; these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods; but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks.ResultsWe evaluated knowledge based and corpus IC based semantic similarity measures derived from SNOMED CT, MeSH, and the UMLS on recently developed semantic similarity benchmarks. Semantic similarity measures based on the UMLS, which contains SNOMED CT and MeSH, significantly outperformed those based solely on SNOMED CT or MeSH across evaluations. Intrinsic IC based measures significantly outperformed path-based and distributional measures. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under http://code.google.com/p/ytex. We provide a publicly-accessible web service to compute semantic similarity, available under http://informatics.med.yale.edu/ytex.web/.ConclusionsKnowledge based semantic similarity measures are more practical to compute than distributional measures, as they do not require an external corpus. Furthermore, knowledge based measures significantly and meaningfully outperformed distributional measures on large semantic similarity benchmarks, suggesting that they are a practical alternative to distributional measures. Future evaluations of semantic similarity measures should utilize benchmarks powered to detect significant differences in measure accuracy.

Project description:BackgroundComputing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem.ResultsWe propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment.ConclusionsOur method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

Dataset Information

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

Publications

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

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