Semantic Description, Publication and Discovery of Workflows in myGrid

The bioinformatics scientific process relies on in silico experiments, which are experiments executed in full in a computational environment. Scientists wish to encode the designs of these experiments as workflows because they provide minimal, declarative descriptions of the designs, overcoming many barriers to the sharing and re-use of these designs between scientists and enable the use of the most appropriate services available at any one time. We anticipate that the number of workflows will increase quickly as more scientists begin to make use of existing workflow construction tools to express their experiment designs. Discovery then becomes an increasingly hard problem, as it becomes more difficult for a scientist to identify the workflows relevant to their particular research goals amongst all those on offer. While many approaches exist for the publishing and discovery of services, there have been few attempts to address where and how authors of experimental designs should advertise the availability of their work or how relevant workflows can be discovered with minimal effort from the user. As the users designing and adapting experiments will not necessarily have a computer science background, we also have to consider how publishing and discovery can be achieved in such a way that they are not required to have detailed technical knowledge of workflow scripting languages. Furthermore, we believe they should be able to make use of others' expert knowledge (the semantics) of the given scientific domain. In this paper, we define the issues related to the semantic description, publishing and discovery of workflows, and demonstrate how the architecture created by the myGrid project aids scientists in this process. We give a walk-through of how users can construct, publish, annotate, discover and enact workflows via the user interfaces of the myGrid architecture; we then describe novel middleware protocols, making use of the Semantic Web technologies RDF and OWL to support workflow publishing and discovery

Building Ontologies in DAML + OIL

In this article we describe an approach to representing and building ontologies advocated by the Bioinformatics and Medical Informatics groups at the University of Manchester. The hand-crafting of ontologies offers an easy and rapid avenue to delivering ontologies. Experience has shown that such approaches are unsustainable. Description logic approaches have been shown to offer computational support for building sound, complete and logically consistent ontologies. A new knowledge representation language, DAML + OIL, offers a new standard that is able to support many styles of ontology, from hand-crafted to full logic-based descriptions with reasoning support. We describe this language, the OilEd editing tool, reasoning support and a strategy for the language’s use. We finish with a current example, in the Gene Ontology Next Generation (GONG) project, that uses DAML + OIL as the basis for moving the Gene Ontology from its current hand-crafted, form to one that uses logical descriptions of a concept’s properties to deliver a more complete version of the ontology

The Montagues and the Capulets

Two households, both alike in dignity, In fair Genomics, where we lay our scene, (One, comforted by its logic's rigour, Claims ontology for the realm of pure, The other, with blessed scientist's vigour, Acts hastily on models that endure), From ancient grudge break to new mutiny, When ‘being’ drives a fly-man to blaspheme. From forth the fatal loins of these two foes, Researchers to unlock the book of life; Whole misadventured piteous overthrows, Can with their work bury their clans' strife. The fruitful passage of their GO-mark'd love, And the continuance of their studies sage, Which, united, yield ontologies undreamed-of, Is now the hour's traffic of our stage; The which if you with patient ears attend, What here shall miss, our toil shall strive to mend

Computer simulations show that Neanderthal facial morphology represents adaptation to cold and high energy demands, but not heavy biting

Three adaptive hypotheses have been forwarded to explain the distinctive Neanderthal face: (i) an improved ability to accommodate high anterior bite forces, (ii) more effective conditioning of cold and/or dry air and, (iii) adaptation to facilitate greater ventilatory demands. We test these hypotheses using three-dimensional models of Neanderthals, modern humans, and a close outgroup (Homo heidelbergensis), applying finite-element analysis (FEA) and computational fluid dynamics (CFD). This is the most comprehensive application of either approach applied to date and the first to include both. FEA reveals few differences between H. heidelbergensis, modern humans, and Neanderthals in their capacities to sustain high anterior tooth loadings. CFD shows that the nasal cavities of Neanderthals and especially modern humans condition air more efficiently than does that of H. heidelbergensis, suggesting that both evolved to better withstand cold and/or dry climates than less derived Homo. We further find that Neanderthals could move considerably more air through the nasal pathway than could H. heidelbergensis or modern humans, consistent with the propositions that, relative to our outgroup Homo, Neanderthal facial morphology evolved to reflect improved capacities to better condition cold, dry air, and, to move greater air volumes in response to higher energetic requirements

The Costs of Carnivory

Mammalian carnivores fall into two broad dietary groups: smaller carnivores (<20 kg) that feed on very small prey (invertebrates and small vertebrates) and larger carnivores (>20 kg) that specialize in feeding on large vertebrates. We develop a model that predicts the mass-related energy budgets and limits of carnivore size within these groups. We show that the transition from small to large prey can be predicted by the maximization of net energy gain; larger carnivores achieve a higher net gain rate by concentrating on large prey. However, because it requires more energy to pursue and subdue large prey, this leads to a 2-fold step increase in energy expenditure, as well as increased intake. Across all species, energy expenditure and intake both follow a three-fourths scaling with body mass. However, when each dietary group is considered individually they both display a shallower scaling. This suggests that carnivores at the upper limits of each group are constrained by intake and adopt energy conserving strategies to counter this. Given predictions of expenditure and estimates of intake, we predict a maximum carnivore mass of approximately a ton, consistent with the largest extinct species. Our approach provides a framework for understanding carnivore energetics, size, and extinction dynamics

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits

MetaMap versus BERT models with explainable active learning: ontology-based experiments with prior knowledge for COVID-19

Emergence of the Coronavirus 2019 Disease has highlighted further the need for timely support for clinicians as they manage severely ill patients. We combine Semantic Web technologies with Deep Learning for Natural Language Processing with the aim of converting human-readable best evi-dence/practice for COVID-19 into that which is computer-interpretable. We present the results of experiments with 1212 clinical ideas (medical terms and expressions) from two UK national healthcare services specialty guides for COVID-19 and three versions of two BMJ Best Practice documents for COVID-19. The paper seeks to recognise and categorise clinical ideas, performing a Named Entity Recognition (NER) task, with an ontology providing extra terms as context and describing the intended meaning of categories understandable by clinicians. The paper investigates: 1) the performance of classical NER using MetaMap versus NER with fine-tuned BERT models; 2) the integration of both NER approaches using a lightweight ontology developed in close collaboration with senior doctors; and 3) the easy interpretation by junior doctors of the main classes from the ontology once populated with NER results. We report the NER performance and the observed agreement for human audits