NETME: on-the-fly knowledge network construction from biomedical literature

Background The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

Automatic consistency assurance for literature-based gene ontology annotation

Background Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. Results In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Conclusions Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.

Medawar and Hamilton on the selective forces in the evolution of ageing

Both Medawar and Hamilton contributed key ideas to the modern evolutionary theory of ageing. In particular, they both suggested that, in populations with overlapping generations, the force with which selection acts on traits declines with the age at which traits are expressed. This decline would eventually cause ageing to evolve. However, the biological literature diverges on the relationship between Medawar’s analysis of the force of selection and Hamilton’s. Some authors appear to believe that Hamilton perfected Medawar’s insightful, yet ultimately erroneous analysis of this force, while others see Hamilton’s analysis as a coherent development of, or the obvious complement to Medawar’s. Here, the relationship between the two analyses is revisited. Two things are argued for. First, most of Medawar’s alleged errors that Hamilton would had rectified seem not to be there. The origin of these perceived errors appears to be in a misinterpretation of Medawar’s writings. Second, the mathematics of Medawar and that of Hamilton show a significant overlap. However, different meanings are attached to the same mathematical expression. Medawar put forth an expression for the selective force on age-specific fitness. Hamilton proposed a full spectrum of selective forces each operating on age-specific fitness components, i.e. mortality and fertility. One of Hamilton’s expressions, possibly his most important, is of the same form as Medawar’s expression. But Hamilton’s selective forces on age-specific fitness components do not add up to yield Medawar’s selective force on age-specific fitness. It is concluded that Hamilton’s analysis should be considered neither as a correction to Medawar’s analysis nor as its obvious complement.

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

Self-Attention Based Models for the Extraction of Molecular Interactions from Biological Texts

For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

Detecting (non)parallel evolution in multidimensional spaces: angles, correlations, and eigenanalysis

Parallelism between evolutionary trajectories in a trait space is often seen as evidence for repeatability of phenotypic evolution, and angles between trajectories play a pivotal role in the analysis of parallelism. However, many biologists have been ignorant on properties of angles in multidimensional spaces, and unsound uses of angles are common in the biological literature. To remedy this situation, this study provides a brief overview on geometric and statistical aspects of angles in multidimensional spaces. Under the null hypothesis that trajectory vectors have no preferred directions, the angle between two independent vectors is concentrated around the right angle, with a more pronounced peak in a higher-dimensional space. This probability distribution is closely related to t- and beta distributions, which can be used for testing the null hypothesis concerning a pair of trajectories. A recently proposed method with eigenanalysis of a vector correlation matrix essentially boils down to the test of no correlation or concentration of multiple vectors, for which a simple test procedure is available in the statistical literature. Concentration of vectors can also be examined by tools of directional statistics such as the Rayleigh test. These frameworks provide biologists with baselines to make statistically justified inferences for (non)parallel evolution.

A catalog of Stratiomyidae (Diptera: Brachycera) from Chile, with a new synonym and notes on the species

A list of all 24 genera and 73 species of Stratiomyidae from Chile is provided, along with all their synonyms and photos of the type specimens of 20 species (including 12 primary types). Only one species is assigned to morphospecies level. All references known to us from the taxonomic and biological literature, including information about name, author, year of publication, page number, type specimens, type locality, and references are given. The geographic distribution of each species is given based on bibliographic and collection data. Three species are removed from the Chilean fauna: Nemotelus tenuivena James, 1974 is only known from the type locality in Argentina; Promeranisa nasuta (Macquart, 1850), which has its type locality corrected to Bolivia, Chiquitos Province; and Ptecticus pomaceus Loew, 1855, referred to Chile due to a locality information error, is a junior synonym of P. trivittatus Say, 1829, syn. nov.

Automatic Consistency Assurance for Literature-based Gene Ontology Annotation

Background: Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. Method: In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. Results and Conclusion: Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Our approach demonstrates clear value for human-in-the-loop curation scenarios

Catalogue of Pyrgotidae (Diptera: Tephritoidea) from Chile

A catalogue of the Pyrgotidae (Diptera) from Chile is provided. All valid names are presented, comprising three species in two genera for the country. All references known to us from the taxonomic and biological literature, to the included names, are provided, including information about name, author, year of publication, page number, type species, type locality, distribution, and references.

Qualitative investigation of trace-based communication: how are traces conceptualised in healthcare teamwork?

ObjectivesThis interview-based qualitative study aims to explore how healthcare providers conceptualise trace-based communication and considers its implications for how teams work. In the biological literature, trace-based communication refers to the non-verbal communication that is achieved by leaving ‘traces’ in the environment and other members sensing them and using them to drive their own behaviour. Trace-based communication is a key component of swam intelligence and has been described as a critical process that enables superorganisms to coordinate work and collectively adapt. This paper brings awareness to its existence in the context of healthcare teamwork.DesignInterview-based study using Constructivist Grounded Theory methodology.SettingThis study was conducted in multiple team contexts at one of Canada’s largest acute-care teaching hospitals.Participants25 clinicians from across professions and disciplines. Specialties included surgery, anesthesiology, psychiatry, internal medicine, geriatrics, neonatology, paramedics, nursing, intensive care, neurology and emergency medicine.InterventionNot relevant due to the qualitative nature of the study.Primary and secondary outcomeNot relevant due to the qualitative nature of the study.ResultsThe dataset was analysed using the sensitising concept of ‘traces’ from Swarm Intelligence. This study brought to light novel and unique elements of trace-based communication in the context of healthcare teamwork including focused intentionality, successful versus failed traces and the contextually bounded nature of the responses to traces. While participants initially felt ambivalent about the idea of using traces in their daily teamwork, they provided a variety of examples. Through these examples, participants revealed the multifaceted nature of the purposes of trace-based communication, including promoting efficiency, preventing mistakes and saving face.ConclusionsThis study demonstrated that clinicians pervasively use trace-based communication despite differences in opinion as to its implications for teamwork and safety. Other disciplines have taken up traces to promote collective adaptation. This should serve as inspiration to at least start exploring this phenomenon in healthcare.