Fully connecting the Observational Health Data Science and Informatics (OHDSI) initiative with the world of linked open data.

Genomics Inform. 2019 Jun;17(2):e13

Authors: Banda JM

Abstract
The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics (OHDSI) initiative combines over 100 controlled vocabularies into its own. However, the OHDSI vocabulary is limited in the sense that it combines multiple terminologies and does not provide a direct way to link them outside of their own self-contained scope. This issue makes the tasks of enriching feature sets by using external resources extremely difficult. In order to address these shortcomings, we have created a linked data version of the OHDSI vocabulary, connecting it with already established linked resources like bioportal, bio2rdf, etc. with the ultimate purpose of enabling the interoperability of resources previously foreign to the OHDSI universe.

PMID: 31307128 [PubMed]

Semalytics: a semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research.

Database (Oxford). 2019 Jan 01;2019:

Authors: Mignone A, Grand A, Fiori A, Medico E, Bertotti A

Abstract
Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient's disease. To achieve this, translational researchers propagate patients' samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori. In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs.

PMID: 31287543 [PubMed - in process]

Semantic Integration and Enrichment of Heterogeneous Biological Databases.

Methods Mol Biol. 2019;1910:655-690

Authors: Sima AC, Stockinger K, de Farias TM, Gil M

Abstract
Biological databases are growing at an exponential rate, currently being among the major producers of Big Data, almost on par with commercial generators, such as YouTube or Twitter. While traditionally biological databases evolved as independent silos, each purposely built by a different research group in order to answer specific research questions; more recently significant efforts have been made toward integrating these heterogeneous sources into unified data access systems or interoperable systems using the FAIR principles of data sharing. Semantic Web technologies have been key enablers in this process, opening the path for new insights into the unified data, which were not visible at the level of each independent database. In this chapter, we first provide an introduction into two of the most used database models for biological data: relational databases and RDF stores. Next, we discuss ontology-based data integration, which serves to unify and enrich heterogeneous data sources. We present an extensive timeline of milestones in data integration based on Semantic Web technologies in the field of life sciences. Finally, we discuss some of the remaining challenges in making ontology-based data access (OBDA) systems easily accessible to a larger audience. In particular, we introduce natural language search interfaces, which alleviate the need for database users to be familiar with technical query languages. We illustrate the main theoretical concepts of data integration through concrete examples, using two well-known biological databases: a gene expression database, Bgee, and an orthology database, OMA.

PMID: 31278681 [PubMed - in process]

FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis.

BMC Bioinformatics. 2019 Jun 27;20(1):359

Authors: Hale ML, Thapa I, Ghersi D

Abstract
BACKGROUND: Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation.
RESULTS: Here we present FunSet, a new web server to perform and visualize enrichment analysis. The web server identifies Gene Ontology terms that are statistically overrepresented in a target set with respect to a background set. The enriched terms are displayed in a 2D plot that captures the semantic similarity between terms, with the option to cluster terms via spectral clustering and identify a representative term for each cluster. FunSet can be used interactively or programmatically, and allows users to download the enrichment results both in tabular form and in graphical form as SVG files or in data format as JSON or csv. To enhance reproducibility of the analyses, users have access to historical data for the ontology and the annotations. The source code for the standalone program and the web server are made available with an open-source license.

PMID: 31248361 [PubMed - in process]

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery.

BMC Bioinformatics. 2019 Jun 10;20(1):306

Authors: Gao Z, Fu G, Ouyang C, Tsutsui S, Liu X, Yang J, Gessner C, Foote B, Wild D, Ding Y, Yu Q

Abstract
BACKGROUND: Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems.
RESULTS: In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks.
CONCLUSIONS: We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

PMID: 31238875 [PubMed - in process]

Organizing phenotypic data-a semantic data model for anatomy.

J Biomed Semantics. 2019 Jun 20;10(1):12

Authors: Vogt L

Abstract
BACKGROUND: Currently, almost all morphological data are published as unstructured free text descriptions. This not only brings about terminological problems regarding semantic transparency, which hampers their re-use by non-experts, but the data cannot be parsed by computers either, which in turn hampers their integration across many fields in the life sciences, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. With an ever-increasing amount of available ontologies and the development of adequate semantic technology, however, a solution to this problem becomes available. Instead of free text descriptions, morphological data can be recorded, stored, and communicated through the Web in the form of highly formalized and structured directed graphs (semantic graphs) that use ontology terms and URIs as terminology.
RESULTS: After introducing an instance-based approach of recording morphological descriptions as semantic graphs (i.e., Semantic Instance Anatomy Knowledge Graphs) and discussing accompanying metadata graphs, I propose a general scheme of how to efficiently organize the resulting graphs in a tuple store framework based on instances of defined named graph ontology classes. The use of such named graph resources allows meaningful fragmentation of the data, which in turn enables subsequent specification of all kinds of data views for managing and accessing morphological data.
CONCLUSIONS: Morphological data that comply with the here proposed semantic data model will not only be computer-parsable but also re-usable by non-experts and could be better integrated with other sources of data in the life sciences. This would allow morphology as a discipline to further participate in eScience and Big Data.

PMID: 31221226 [PubMed - in process]

Breaking Winner-takes-all: Iterative-winners-out Networks for Weakly Supervised Temporal Action Localization.

IEEE Trans Image Process. 2019 Jun 17;:

Authors: Zeng R, Gan C, Chen P, Huang W, Wu Q, Tan M

Abstract
We address the challenging problem of weakly supervised temporal action localization from unconstrained web videos, where only the video-level action labels are available during training. Inspired by the Adversarial Erasing strategy in weakly supervised semantic segmentation, we propose a novel iterative-winners-out network. Specifically, we make two technical contributions: 1) we propose an iterative training strategy, namely winners-out, to select the most discriminative action instances in each training iteration and remove them in the next training iteration. This iterative process alleviates the "winner-takes-all" phenomenon that existing approaches tend to choose the video segments that strongly correspond to the video label, but neglect other less discriminative video segments. With this strategy, our network is able to localize not only the most discriminative instances but also the less discriminative ones. 2) to better select the target action instances in winners-out, we devise a class-discriminative localization technique. By employing the attention mechanism and the information learned from data, our technique is able to identify the most discriminative action instances effectively. The two key components are integrated into an end-to-end network to localize actions without using the frame-level annotations. Extensive experimental results demonstrate that our method outperforms the state-of-the-art weakly supervised approaches on ActivityNet1.3 and improves mAP from 16.9% to 20.5% on THUMOS14. Notably, even with weak video-level supervision, our method attains comparable accuracy to those employing frame-level supervisions.

PMID: 31217119 [PubMed - as supplied by publisher]

Semantic Web Technologies for Sharing Clinical Information in Health Care Systems.

Acta Inform Med. 2019 Mar;27(1):4-7

Authors: Karami M, Rahimi A

Abstract
Introduction: Semantic Web (SW) technologies is capable of facilitating the management and sharing of knowledge and promote semantic interoperability among healthcare information systems.
Aim: This article is designed to provide an overview of the SW technologies.
Methods: This article was performed based on a literature review and Internet search through scientific databases such as PubMed, Scopus, Web of Science and Google Scholar.
Result: The literature on SW addresses the technical and content aspects of SW technologies including description of ontology, interoperability standards in SW, creating ontology, types of ontologies, ontology editors, ontologies in healthcare.
Discussion: The discussion on this forum aims to help understand the benefits of SW technologies in healthcare.
Conclusion: SW promotes a shift from the "syntactic" level to the "semantic" level of services, applications, and people and finally to pragmatic level by sharing knowledge among clinicians, researchers and healthcare providers.

PMID: 31213735 [PubMed]

Policies and Programs for the Prevention and Control of Breast Cancer in Mexican and Latin American Women: Protocol for a Scoping Review.

JMIR Res Protoc. 2019 Jun 12;8(6):e12624

Authors: Ramos Herrera IM

Abstract
BACKGROUND: Breast cancer has become a major public health problem around the world, especially in Mexico and Latin America. Screening for breast cancer, which involves self-examination, mammography, and clinical breast examination, is crucial for early diagnosis, which in turn is associated with improved outcomes and survival rates. Although breast cancer prevention and control activities are being implemented in Mexico and Latin America, as in many other countries, there are no comprehensive public reports that provide information on the number, type, and scope of these activities; the impact of the programs and actions implemented; and the policies that form the basis of these programs.
OBJECTIVE: This study aims to present the design of a protocol for a scoping review on the policies and action programs for breast cancer care in Mexico and Latin America, as well as their objectives and implementation plans.
METHODS: This scoping review is guided by the methodological reference framework proposed by Arksey and O'Malley. A systematic search of the following electronic databases will be performed: MEDLINE (PubMed), MEDLINE (EBSCOHost), CINAHL (EBSCOHost), Academic Search Complete (EBSCOHost), ERIC, ISI Web of Science (Science Citation Index) in English and Cochrane and MEDES-MEDicina in Spanish. A search will be conducted to identify relevant studies published between 2000 and 2018. Data will be analyzed and presented in descriptive statistics and qualitative content analyses with analysis matrices and semantic networks. The selected studies will be arranged according to the Specific Action Program, Prevention and Control of Female Cancer 2013-2018.
RESULTS: The intention is to perform this review during the first and second quarters of 2019 and present the results to health authorities by the first quarter of 2020. Results will also be sent for publication to an indexed journal by the second quarter of 2020.
CONCLUSIONS: We present a protocol for a scoping review-type literature revision based on the Arksey and O'Malley methodology to be performed during the first quarter of 2019. According to this 6-stage methodology, we will identify the scientific publications that present or analyze first-level action policies and programs for breast cancer care in Mexican women, as well as the results of these policies and programs, if any. The outcome of this review will be used to define the basis of a research project intended to design an educational intervention strategy for the general public in Mexico to enable them to deal with this public health problem.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/12624.

PMID: 31199301 [PubMed]

Modeling early lexico-semantic network development: Perceptual features matter most.

J Exp Psychol Gen. 2019 Apr;148(4):763-782

Authors: Peters R, Borovsky A

Abstract
What aspects of word meaning are important in early word learning and lexico-semantic network development? Adult lexico-semantic systems flexibly encode multiple types of semantic features, including functional, perceptual, taxonomic, and encyclopedic. However, various theoretical accounts of lexical development differ on whether and how these semantic properties of word meanings are initially encoded into young children's emerging lexico-semantic networks. Whereas some accounts highlight the importance of early perceptual versus conceptual properties, others posit that thematic or functional aspects of word meaning are primary relative to taxonomic knowledge. We seek to shed light on these debates with 2 modeling studies that explore patterns in early word learning using a large database of early vocabulary in 5,450 children, and a newly developed set of semantic features of early acquired nouns. In Study 1, we ask whether semantic properties of early acquired words relate to order in which these words are typically learned; Study 2 models normative lexico-semantic noun-feature network development compared to random network growth. Both studies provide converging evidence that perceptual properties of word meanings play a key role in early word learning and lexico-semantic network development. The findings lend support to theoretical accounts of language learning that highlight the importance of the child's perceptual experience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

PMID: 30973265 [PubMed - indexed for MEDLINE]

Semantic Processing.

Adv Exp Med Biol. 2019;1137:61-91

Authors: Couto FM

Abstract
In the previous chapter we were able to automatically process text by recognizing a limited set of entities. This chapter will introduce the world of semantics, and present step-by-step examples to retrieve and enhance text and data processing by using semantics. The goal is to equip the reader with the basic set of skills to explore semantic resources that are nowadays available using simple shell script commands.

PMID: 31183820 [PubMed - in process]

Resources.

Adv Exp Med Biol. 2019;1137:9-15

Authors: Couto FM

Abstract
The previous chapter presented the importance of text and semantic resources for Health and Life studies. This chapter will describe what kind of text and semantic resources are available, where they can be found, and how they can be accessed and retrieved.

PMID: 31183817 [PubMed - in process]

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

Nutrients. 2019 Jun 08;11(6):

Authors: Yang C, Ambayo H, Baets B, Kolsteren P, Thanintorn N, Hawwash D, Bouwman J, Bronselaer A, Pattyn F, Lachat C

Abstract
BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology.
METHODS: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts.
RESULTS: Ontologies for "food and nutrition" (n = 37), "disease and specific population" (n = 100), "data description" (n = 21), "research description" (n = 35), and "supplementary (meta) data description" (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts.
CONCLUSION: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

PMID: 31181762 [PubMed - in process]

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research.

Sci Data. 2019 Jun 03;6(1):79

Authors: Kafkas Ş, Abdelhakim M, Hashish Y, Kulmanov M, Abdellatif M, Schofield PN, Hoehndorf R

Abstract
Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.

PMID: 31160594 [PubMed - in process]

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.

Front Pharmacol. 2019;10:415

Authors: Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V

Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing "knowledge-intensive" systems, depending on a conceptual "knowledge" schema and some kind of "reasoning" process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

PMID: 31156424 [PubMed]

An Ontology and Semantic Web Service for Quantum Chemistry Calculations.

J Chem Inf Model. 2019 May 31;:

Authors: Krdzavac N, Mosbach S, Nurkowski D, Buerger P, Akroyd J, Martin J, Menon A, Kraft M

Abstract
The purpose of this article is to present an ontology, termed OntoCompChem, for quantum chemistry calculations as performed by the Gaussian quantum chemistry software, as well as a semantic web service named MolHub. The OntoCompChem ontology has been developed based on the semantics of concepts specified in the CompChem convention of Chemical Markup Language (CML) and by extending the Gainesville Core (GNVC) ontology. MolHub is developed in order to establish semantic interoperability between different tools used in quantum chemistry and thermochemistry calculations, and as such is integrated into the J-Park Simulator (JPS) -- a multi-domain interactive simulation platform and expert system. It uses the OntoCompChem ontology and implements a formal language based on propositional logic as a part of its query engine, which verifies satisfiability through reasoning. This paper also presents a NASA polynomial use-case scenario to demonstrate semantic interoperability between Gaussian and a tool for thermodynamic data calculations within MolHub.

PMID: 31150242 [PubMed - as supplied by publisher]

Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques.

JMIR Ment Health. 2019 May 21;6(5):e13498

Authors: Brenas JH, Shin EK, Shaban-Nejad A

Abstract
BACKGROUND: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond.
OBJECTIVE: To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs' surveillance and research.
METHODS: We use advanced knowledge representation and semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2).
RESULTS: The ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies.
CONCLUSIONS: The ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs' surveillance and evaluation.

PMID: 31115344 [PubMed]

BEERE: a web server for biomedical entity expansion, ranking and explorations.

Nucleic Acids Res. 2019 May 22;:

Authors: Yue Z, Willey CD, Hjelmeland AB, Chen JY

Abstract
BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. 'biomedical entities', in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity-a gene or a term-within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype-phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

PMID: 31114876 [PubMed - as supplied by publisher]

SBOL-OWL: An ontological approach for formal and semantic representation of synthetic biology information.

ACS Synth Biol. 2019 May 06;:

Authors: Misirli G, Taylor R, Goñi-Moreno A, Mclaughlin JA, Myers CJ, Gennari J, Lord P, Wipat A

Abstract
Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies.

PMID: 31059645 [PubMed - as supplied by publisher]

Feature engineering for sentiment analysis in e-health forums.

PLoS One. 2018;13(11):e0207996

Authors: Carrillo-de-Albornoz J, Rodríguez Vidal J, Plaza L

Abstract
INTRODUCTION: Exploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion mining. Even though the classification of patient opinions into positive and negative has been previously tackled, most works make use of machine learning methods and bags of words. Our first contribution is an extensive evaluation of different features, including lexical, syntactic, semantic, network-based, sentiment-based and word embeddings features to represent patient-authored texts for polarity classification. The second contribution of this work is the study of polar facts (i.e. objective information with polar connotations). Traditionally, the presence of polar facts has been neglected and research in polarity classification has been bounded to opinionated texts. We demonstrate the existence and importance of polar facts for the polarity classification of health information.
MATERIAL AND METHODS: We annotate a set of more than 3500 posts to online health forums of breast cancer, crohn and different allergies, respectively. Each sentence in a post is manually labeled as "experience", "fact" or "opinion", and as "positive", "negative" and "neutral". Using this data, we train different machine learning algorithms and compare traditional bags of words representations with word embeddings in combination with lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-authored contents into positive, negative and neutral. Beside, we experiment with a combination of textual and semantic representations by generating concept embeddings using the UMLS Metathesaurus.
RESULTS: We reach two main results: first, we find that it is possible to predict polarity of patient-authored contents with a very high accuracy (≈ 70 percent) using word embeddings, and that this considerably outperforms more traditional representations like bags of words; and second, when dealing with medical information, negative and positive facts (i.e. objective information) are nearly as frequent as negative and positive opinions and experiences (i.e. subjective information), and their importance for polarity classification is crucial.

PMID: 30496232 [PubMed - indexed for MEDLINE]