Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers

BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

<p>Abstract</p> <p>Background</p> <p>High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.</p> <p>Results</p> <p>We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal <url>http://bioportal.bioontology.org/ontologies/44531</url>.</p> <p>Conclusions</p> <p>After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.</p

Preanalytics: online-training for medical assistants – international study on demand, benefit and sustainability

Hintergrund: Bei Laboruntersuchungen entstehen die meisten Fehler in der präanalytischen Phase. Im ambulanten Bereich sind Medizinische Fachangestellte (MFA) für die Gewinnung und Handhabung von Proben zuständig. Methoden: In einer internationalen Kohortenstudie prüften wir die Fragen 1. Wie viele Medizinische Fachangestellte (MFA, Deutschland) und Praxisassistentinnen (MPA, Schweiz) lassen sich für eine Fortbildung gewinnen? 2. Wie ist ihr Kenntnisstand von Präanalytik? und die Hypothese: Mit einer Online-Fortbildung kann ein signifikanter akuter und nachhaltiger Wissenszuwachs erreicht werden. Dazu wurde MFA und MPA eine kostenlose „Online-Fortbildung Präanalytik bei Blut- und Probeentnahmen“ angeboten. Sie umfasste drei Tests (T1–T3) mit Single- und Multiple Choice Fragen und eine Lerneinheit zwischen T1 und T2. Alle Tests enthielten dieselben Fragen. T3 fand drei Monate nach T2 statt. Bei 60% richtigen Antworten in T3 wurde ein Zertifikat vergeben. Ergebnisse: Es registrierten sich 332 Personen für die Fortbildung, 262 nahmen teil, 199 Datensätze waren statistisch auswertbar. In T1 erzielten 54,7% der TeilnehmerInnen 60% richtige Antworten, in T 2 waren es 94,97% und drei Monate später in T3 92,45%. Schlussfolgerung: Die Kenntnisse von MFA und MPA zur Präanalytik scheinen unzureichend zu sein. Nur 0,15% der Angesprochenen haben an der Qualifizierung teilgenommen. Die Online-Fortbildung konnte einen nachhaltigen Wissenszuwachs zur Präanalytik vermitteln

TIN-X:target importance and novelty explorer

Abstract Motivation The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. Results We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. Availability and Implementation http://www.newdrugtargets.org </jats:sec

Representing Semantified Biological Assays in the Open Research Knowledge Graph

In the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation of semantified bioassays in the Open Research Knowledge Graph (ORKG). In particular, we describe a semantification system work-in-progress to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.Comment: In Proceedings of 'The 22nd International Conference on Asia-Pacific Digital Libraries

The fourteenth-century poll tax returns and the study of English surname distribution

The modern-day distributions of English surnames have been considered in genealogical, historical, and philological research as possible indicators of their origins. However, many centuries have passed since hereditary surnames were first used, and so their distribution today does not necessarily reflect their original spread, misrepresenting their origins. Previously, medieval data with national coverage have not been available for a study of surname distribution, but with the recent publication of the fourteenth-century poll tax returns, this has changed. By presenting discrepancies in medieval and nineteenth-century distributions, it is shown that more recent surname data may not be a suitable guide to surname origins and can be usefully supplemented by medieval data in order to arrive at more accurate conclusions

Effectiveness of Lumbar Cerebrospinal Fluid Drain Among Patients With Aneurysmal Subarachnoid Hemorrhage: A Randomized Clinical Trial.

IMPORTANCE After aneurysmal subarachnoid hemorrhage, the use of lumbar drains has been suggested to decrease the incidence of delayed cerebral ischemia and improve long-term outcome. OBJECTIVE To determine the effectiveness of early lumbar cerebrospinal fluid drainage added to standard of care in patients after aneurysmal subarachnoid hemorrhage. DESIGN, SETTING, AND PARTICIPANTS The EARLYDRAIN trial was a pragmatic, multicenter, parallel-group, open-label randomized clinical trial with blinded end point evaluation conducted at 19 centers in Germany, Switzerland, and Canada. The first patient entered January 31, 2011, and the last on January 24, 2016, after 307 randomizations. Follow-up was completed July 2016. Query and retrieval of data on missing items in the case report forms was completed in September 2020. A total of 20 randomizations were invalid, the main reason being lack of informed consent. No participants meeting all inclusion and exclusion criteria were excluded from the intention-to-treat analysis. Exclusion of patients was only performed in per-protocol sensitivity analysis. A total of 287 adult patients with acute aneurysmal subarachnoid hemorrhage of all clinical grades were analyzable. Aneurysm treatment with clipping or coiling was performed within 48 hours. INTERVENTION A total of 144 patients were randomized to receive an additional lumbar drain after aneurysm treatment and 143 patients to standard of care only. Early lumbar drainage with 5 mL per hour was started within 72 hours of the subarachnoid hemorrhage. MAIN OUTCOMES AND MEASURES Primary outcome was the rate of unfavorable outcome, defined as modified Rankin Scale score of 3 to 6 (range, 0 to 6), obtained by masked assessors 6 months after hemorrhage. RESULTS Of 287 included patients, 197 (68.6%) were female, and the median (IQR) age was 55 (48-63) years. Lumbar drainage started at a median (IQR) of day 2 (1-2) after aneurysmal subarachnoid hemorrhage. At 6 months, 47 patients (32.6%) in the lumbar drain group and 64 patients (44.8%) in the standard of care group had an unfavorable neurological outcome (risk ratio, 0.73; 95% CI, 0.52 to 0.98; absolute risk difference, -0.12; 95% CI, -0.23 to -0.01; P = .04). Patients treated with a lumbar drain had fewer secondary infarctions at discharge (41 patients [28.5%] vs 57 patients [39.9%]; risk ratio, 0.71; 95% CI, 0.49 to 0.99; absolute risk difference, -0.11; 95% CI, -0.22 to 0; P = .04). CONCLUSION AND RELEVANCE In this trial, prophylactic lumbar drainage after aneurysmal subarachnoid hemorrhage lessened the burden of secondary infarction and decreased the rate of unfavorable outcome at 6 months. These findings support the use of lumbar drains after aneurysmal subarachnoid hemorrhage. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01258257