Ontology-based annotations and semantic relations in large-scale (epi)genomics data

In the domain of biomedical and health informatics, ontologies are widely used to capture knowledge ranging from bioinformatics such as gene, protein, protein interactions, etc. to clinical/healthcare informatics knowledge such as diseases, symptoms, treatment, medication, etc. Currently, one medical knowledge source that encapsulates a broad spectrum of medical knowledge is the Unified Medical Language System (UMLS), which can be defined as a compendium of diverse medical ontological standards. The primary components of the UMLS are: Semantic Network (UMLS-SN) – designed by interconnecting well-defined semantic types with semantic relationships, and Metathesaurus (UMLS-META) – the base of UMLS system that is comprised of millions of medical concepts from diverse medical standards. However, within the biomedical and health informatics community, the concepts of software engineering and domain modeling (using meta-models such as ERD, UML, and XML) are very successful in designing and implementing biomedical/health domain application models. In the current status, the UMLS-SN is primarily employed for classification of medical concepts in UMLS-META, but UMLS-SN knowledge can't be viewed or employed as a modeling framework for designing ontological models and is restricted to the UMLS environment. Thus, the impact of the biomedical semantics captured by UMLS-SN might be minimal in medical facilities, research and healthcare organizations that are highly influenced by software engineering, meta-models and domain model-based practices. In order fill this gap, the authors propose a meta-modeling framework for UMLS-SN based on the UML Profile (built using UML meta-model) that will result in a customized domain specific meta-model. This specialized meta-model that encapsulates the medical knowledge semantics of UMLS-SN can then be employed for designing ontological models or relevant healthcare application models and simultaneously be coherent with software meta-models and domain modeling practices.

Mapping clinical attributes from hospital information systems to standardized terminologies may allow their scientific reuse for multicenter studies. The Unified Medical Language System (UMLS) defines synonyms in different terminologies, which could be valuable for achieving semantic interoperability between different sites. Here we aim to explore the potential relevance of UMLS concepts and associated semantic relations for widely used clinical terminologies in a German university hospital. To semi-automatically examine a sample of the 200 most frequent codes from Erlangen University Hospital for three relevant terminologies, we implemented a script that queries their UMLS representation and associated mappings via a programming interface. We found that 94% of frequent diagnostic codes were available in UMLS, and that most of these codes could be mapped to other terminologies such as SNOMED CT. We observed that all examined laboratory codes were represented in UMLS, and that various translations to other languages were available for these concepts. The classification that is most widely used in German hospital for documenting clinical procedures was not originally represented in UMLS, but external mappings to SNOMED CT allowed identifying UMLS entries for 90.5% of frequent codes. Future research could extend this investigation to other code sets and terminologies, or study the potential utility of available mappings for specific applications.

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Tomato Functional Genomics Database (TFGD) provides a comprehensive resource to store, query, mine, analyze, visualize and integrate large-scale tomato functional genomics data sets. The database is functionally expanded from the previously described Tomato Expression Database by including metabolite profiles as well as large-scale tomato small RNA (sRNA) data sets. Computational pipelines have been developed to process microarray, metabolite and sRNA data sets archived in the database, respectively, and TFGD provides downloads of all the analyzed results. TFGD is also designed to enable users to easily retrieve biologically important information through a set of efficient query interfaces and analysis tools, including improved array probe annotations as well as tools to identify co-expressed genes, significantly affected biological processes and biochemical pathways from gene expression data sets and miRNA targets, and to integrate transcript and metabolite profiles, and sRNA and mRNA sequences. The suite of tools and interfaces in TFGD allow intelligent data mining of recently released and continually expanding large-scale tomato functional genomics data sets. TFGD is available at http://ted.bti.cornell.edu.

In the recent few years, neural-network-based word embeddings have been widely used in text mining. However, the dense representations of word embeddings act as a black box and lack interpretability. Even though word embeddings are able to capture semantic regularities in free text documents, it is not clear what kinds of semantic relations can be represented by word embeddings and how semantically-related terms can be retrieved from word embeddings. In this study, we propose a novel approach to explore the semantic relations in neural embeddings using extrinsic knowledge from WordNet and Unified Medical Language System (UMLS). We trained multiple word embeddings using health-related articles in Wikipedia. We then evaluated the performance of the different word embeddings in semantic relation term retrieval tasks. This study shows that word embeddings can group terms with diverse semantic relations together.

Automated Analysis and Codification of Free Text Pathology Reports

This paper describes first results using the Unified Medical Language System (UMLS) for distantly supervised relation extraction. UMLS is a large knowledge base which contains information about millions of medical concepts and relations between them. Our approach is evaluated using existing relation extraction data sets that contain relations that are similar to some of those in UMLS.

Most methods for large-scale gene expression microarray and RNA-Seq data analysis are designed to determine the lists of genes or gene products that show distinct patterns and/or significant differences. The most challenging and rate-liming step, however, is to determine what the resulting lists of genes and/or transcripts biologically mean. Biomedical ontology and pathway-based functional enrichment analysis is widely used to interpret the functional role of tightly correlated or differentially expressed genes. The groups of genes are assigned to the associated biological annotations using Gene Ontology terms or biological pathways and then tested if they are significantly enriched with the corresponding annotations. Unlike previous approaches, Gene Set Enrichment Analysis takes quite the reverse approach by using pre-defined gene sets. Differential co-expression analysis determines the degree of co-expression difference of paired gene sets across different conditions. Outcomes in DNA microarray and RNA-Seq data can be transformed into the graphical structure that represents biological semantics. A number of biomedical annotation and external repositories including clinical resources can be systematically integrated by biological semantics within the framework of concept lattice analysis. This array of methods for biological knowledge assembly and interpretation has been developed during the past decade and clearly improved our biological understanding of large-scale genomic data from the high-throughput technologies.

Data mining is a process of discovering useful information from a database and analysis of extracted information. Text mining uses many techniques of data mining. It primarily deals with unstructured data. Web mining is an extension of text mining since it deals with unstructured data. Data mining relates to find data from “static databases” which contains “structured” data where as, web mining plays with data that are “dynamic” and “unstructured”. In this papers our goal is to mine biomedical data from hypertext documents (e.g., mining data from web contents) using text mining techniques with the help of “biomedical ontology”. Web data repositories are the hypertext documents. Texts in the Hypertext documents are unstructured and they contain Hypertext Markup Language (HTML) tags, scripting languages, images, audios, videos, URLs etc. We collect a number of documents using Google crawler and preprocess the hypertext documents and extract the text data. Next, we identify whether a word is a biomedical entity or not by using a biomedical database the “Unified Medical Language System (UMLS) metathesaurus”. The mapping of biomedical entity from the metathesaurus will be done based on keyword query. Then we apply the result to re-rank the web documents to find most relevant documents. We conclude that the more occurrence of a biomedical entity in a page, the more relevant the page is, and thus, we can re-rank the documents to find the most relevant documents by using text mining technique.

Quantifying the association between biomedical terms play an essential role in medical text processing. It helps to extract biomedical information and discover similarities between medical categories/concepts. Correlation measures determine the probability of two terms having a significant co-occurrence pattern together by chance or otherwise. It is based on the frequency of occurrence of the two individual terms and the coincidence of their mutual frequency. Some researchers have proposed techniques of semantic similarity and measuring semantic correlation scores using a unified medical language system (UMLS) in the biomedical field through many experiments and proven methods of validation of applicability. The current chapter proposes using the semantic similarity measurement between two medical terms in a single ontology or multiple ontologies in the International Classification of Diseases (ICD-10 V1.0) as a primary source. We use a comparison study to evaluate the obtained results and the results of human experts based on the correlation coefficient to verify the validity of the proposed method. The results show the possibility of efficiently implementing the proposed approach to measure the similarity in the biomedical domain.

No. 478 Procedure dictations to t-test: leveraging natural language parsing in interventional radiology for extracting clinically significant medical data from unstructured dictations

We present an approach to using ontologies as interlingua in cross-language information retrieval in the medical domain. Our approach is based on using the Unified Medical Language System (UMLS) as the primary ontology. Documents and queries are annotated with multiple layers of linguistic information (part-of-speech tags, lemmas, phrase chunks). Based on this we identify medical terms and semantic relations between them and map them to their position in the ontology. The paper describes experiments in monolingual and cross-language document retrieval, performed on a corpus of medical abstracts. Results show that semantic information, specifically the combined use of concepts and relations, increases the precision in monolingual retrieval. In cross-language retrieval the semantic annotation outperforms machine translation of the queries, but the best results are achieved by combining a similarity thesaurus with the semantic codes.

Visionary medical informaticist who laid the groundwork for PubMed

The PAP900 dataset centers on the semantic relationship between affective words in Portuguese. It contains 900 word pairs, each annotated by at least 30 human raters for both semantic similarity and semantic relatedness. In addition to the semantic ratings, the dataset includes the word categorization used to build the word pairs and detailed sociodemographic information about annotators, enabling the analysis of the influence of personal factors on the perception of semantic relationships. Furthermore, this article describes in detail the dataset construction process, from word selection to agreement metrics.Data was collected from Portuguese university psychology students, who completed two rounds of questionnaires. In the first round annotators were asked to rate word pairs on either semantic similarity or relatedness. The second round switched the relation type for most annotators, with a small percentage being asked to repeat the same relation. The instructions given emphasized the differences between semantic relatedness and semantic similarity, and provided examples of expected ratings of both.There are few semantic relations datasets in Portuguese, and none focusing on affective words. PAP900 is distributed in distinct formats to be easy to use for both researchers just looking for the final averaged values and for researchers looking to take advantage of the individual ratings, the word categorization and the annotator data. This dataset is a valuable resource for researchers in computational linguistics, natural language processing, psychology, and cognitive science.