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Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main. Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.

In this paper we analyse the classification of zoological illustrations. Historically, zoological illustrations were the modus operandi for the documentation of new species, an Currently, they serve as crucial sources for long-term ecological and biodiversity research. By employing computational methods for classification, the illustrations can be made amenable to research. Automated species identification is challenging due to the long-tailed nature of the data, and the millions of possible classes in the species taxonomy. Success commonly depends on large training sets with many examples per class, but images from only a subset of classes are digitally available, and many images are unlabelled, since labelling requires domain expertise. We explore zero-shot learning to address the problem, where features are learned from classes with medium to large samples, which are then transferred to recognise classes with few or no training samples. We specifically explore how distributed, multimodal background knowledge from data providers, such as the Global Biodiversity Information Facility (GBIF), iNaturalist, and the Biodiversity Heritage Library (BHL), can be used to share knowledge between classes for zero-shot learning. We train a prototypical network for zero-shot classification, and introduce fused prototypes (FP) and hierarchical prototype loss (HPL) to optimise the model. Finally, we analyse the performance of the model for use in real-world applications. The experimental results are encouraging, indicating potential for use of such models in an expert support system, but also express the difficulty of our task, showing a necessity for research into computer vision methods that are able to learn from small samples.

Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

Motivation: The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level hallmarks concepts into data level-links between individual genes and individual cancer hallmarks varies widely between studies. When we examine different strategies for linking and mapping cancer hallmarks in detail, we see significant differences, but also consensus. Results: Here we compare hallmark mapping schemes from multiple studies and explore the consensus knowledge from these different approaches, in order to help us better understand the core biological processes and pathways that are associated with the hallmarks of cancer. We also explore the differences between mapping schemes and identify which differences represent changes in our understanding of cancer, changes in our understanding of biological processes in the non-disease state, or the accumulation of more experimental evidence over time. Conclusions: Mapping strategies rely on intermediate knowledge resources, such as biological pathway databases like KEGG or the Gene Ontology. The structure and annotations of these intermediate resources also change over time. The results of this study therefore highlight the challenges of integrating accumulated, distributed and changing biological knowledge in bioinformatics.

Biodiversity research expeditions to the globe’s most biodiverse areas have been conducted for several hundred years. Natural history museums contain a wealth of historical materials from such expeditions, but they are stored in a fragmented way. As a consequence links between the various resources, e.g., specimens, illustrations and field notes, are often lost and are not easily re-established. Natural history museums have started to use persistent identifiers for physical collection objects, such as specimens, as well as associated information resources, such as web pages and multimedia. As a result, these resources can more easily be linked, using Linked Open Data (LOD), to information sources on the web. Specimens can be linked to taxonomic backbones of data providers, e.g., the Encyclopedia Of Life (EOL), the Global Biodiversity Information Facility (GBIF), or publications with Digital Object Identifiers (DOI). For the content of biodiversity expedition archives, (e.g. field notes), no such formalisations exist. However, linking the specimens to specific handwritten notes taken in the field can increase their scientific value. Specimens are generally accompanied by a label containing the location of the site where the specimen was collected, the collector’s name and the classification. Field notes often augment the basic metadata found with specimens with important details concerning, for instance, an organism’s habitat and morphology. Therefore, inter-collection interoperability of multimodal resources is just as important as intra-collection interoperability of unimodal resources. The linking of field notes and illustrations to specimens entails a number of challenges: historical handwritten content is generally difficult to read and interpret, especially due to changing taxonomic systems, nomenclature and collection practices. It is vital that: the content is structured in a similar way as the specimens, so that links can more easily be re-established either manually or in an automated way; for consolidation, the content is enriched with outgoing links to semantic resources, such as Geonames or Virtual International Authority File (VIAF); and this process is a transparent one: how links are established, why and by whom, should be stored to encourage scholarly discussions and to promote the attribution of efforts. the content is structured in a similar way as the specimens, so that links can more easily be re-established either manually or in an automated way; for consolidation, the content is enriched with outgoing links to semantic resources, such as Geonames or Virtual International Authority File (VIAF); and this process is a transparent one: how links are established, why and by whom, should be stored to encourage scholarly discussions and to promote the attribution of efforts. In order to address some of these issues, we have built a tool, the Semantic Field Book Annotator (SFB-A), that allows for the direct annotation of digitised (scanned) pages of field books and illustrations with Linked Open Data (LOD). The tool guides the user through the annotation process, so that semantic links are automatically generated in a formalised way. These annotations and links are subsequently stored in an RDF triplestore. As the use of the Darwin Core standard is considered best practice among collection managers for the digitisation of their specimens, our tool is equipped with an ontology based on Darwin Core terms, the NHC-Ontology, which extends the Darwin Semantic Web (DSW) ontology. The tool can annotate any image, be it an image of a specimen with a textual label, an illustration with a textual label or a handwritten species description. Interoperability of annotations between the various resources within a collection is therefore ensured. Terms in the ontology are structured using OWL web ontology language. This allows for more complex tasks such as OWL reasoning and semantic queries, and facilitates the creation of a richer knowledge base that is more amenable to research.

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Large collections of historical biodiversity expeditions are housed in natural history museums throughout the world. Potentially they can serve as rich sources of data for cultural historical and biodiversity research. However, they exist as only partially catalogued specimen repositories and images of unstructured, non-standardised, hand-written text and drawings. Although many archival collections have been digitised, disclosing their content is challenging. They refer to historical place names and outdated taxonomic classifications and are written in multiple languages. Efforts to transcribe the hand-written text can make the content accessible, but semantically describing and interlinking the content would further facilitate research. We propose a semantic model that serves to structure the named entities in natural history archival collections. In addition, we present an approach for the semantic annotation of these collections whilst documenting their provenance. This approach serves as an initial step for an adaptive learning approach for semi-automated extraction of named entities from natural history archival collections. The applicability of the semantic model and the annotation approach is demonstrated using image scans from a collection of 8, 000 field book pages gathered by the Committee for Natural History of the Netherlands Indies between 1820 and 1850, and evaluated together with domain experts from the field of natural and cultural history.

Geographical and taxonomical referencing of specimens and documented species observations from within and across natural history collections is vital for ongoing species research. However, much of the historical data such as field books, diaries and specimens, are challenging to work with. They are computationally inaccessable, refer to historical place names and taxonomies, and are written in a variety of languages. In order to address these challenges and elucidate historical species observation data, we developed a workflow to (i) crowd-source semantic annotations from handwritten species observations, (ii) transform them into RDF (Resource Description Framework) and (iii) store and link them in a knowledge base. Instead of full-transcription we directly annotate digital field books scans with key concepts that are based on Darwin Core standards. Our workflow stresses the importance of verbatim annotation. The interpretation of the historical content, such a resolving a historical taxon to a current one, can be done by individual researchers after the content is published as linked open data. Through the storage of annotion provenance, who created the annotation and when, we allow multiple interpretations of the content to exist in parallel, stimulating scientific discourse. The semantic annotation process is supported by a web application, the Semantic Field Book (SFB)-Annotator, driven by an application ontology. The ontology formally describes the content and meta-data required to semantically annotate species observations. It is based on the Darwin Core standard (DwC), Uberon and the Geonames ontology. The provenance of annotations is stored using the Web Annotation Data Model. Adhering to the principles of FAIR (Findable, Accessible, Interoperable & Reusable) and Linked Open Data, the content of the specimen collections can be interpreted homogeneously and aggregated across datasets. This work is part of the Making Sense project: makingsenseproject.org. The project aims to disclose the content of a natural history collection: a 17,000 page account of the exploration of the Indonesian Archipelago between 1820 and 1850 (Natuurkundige Commissie voor Nederlands-Indie) With a knowledge base, researchers are given easy access to the primary sources of natural history collections. For their research, they can aggregate species observations, construct rich queries to browse through the data and add their own interpretations regarding the meaning of the historical content.

In many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship.

International audience; To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

The goal of developing therapies and dosage regimes for characterized subgroups of the general population can be facilitated by the use of simulation models able to incorporate information about inter-individual variability in drug disposition (pharmacokinetics), toxicity and response effect (pharmacodynamics). Such observed variability can have multiple causes at various scales, ranging from gross anatomical differences to differences in genome sequence. Relevant data for many of these aspects, particularly related to molecular assays (known as ‘-omics’), are available in online resources, but identification and assignment to appropriate model variables and parameters is a significant bottleneck in the model development process. Through its efforts to standardize annotation with consequent increase in data usability, the human physiome project has a vital role in improving productivity in model development and, thus, the development of personalized therapy regimes. Here, we review the current status of personalized medicine in clinical practice, outline some of the challenges that must be overcome in order to expand its applicability, and discuss the relevance of personalized medicine to the more widespread challenges being faced in drug discovery and development. We then review some of (i) the key data resources available for use in model development and (ii) the potential areas where advances made within the physiome modelling community could contribute to physiologically based pharmacokinetic and physiologically based pharmacokinetic/pharmacodynamic modelling in support of personalized drug development. We conclude by proposing a roadmap to further guide the physiome community in its on-going efforts to improve data usability, and integration with modelling efforts in the support of personalized medicine development.

Managing paleoscience data is highly challenging to the multiplicity of actors in play, types of sampling, analysis, post-analysis treatments, statistics etc. However, a well-structured curating of data would permit innovative developments based on data and/or sample re-use, such as meta-analysis or the development of new proxies on previously studied cores. In this paper, we will present two recent initiatives that allowed us tackling this objective at a French national level: the “National Cyber Core Repository” (NCCR) and the “LTER-France retro-observatory” (ROZA).NCCR was developed under the umbrella of the French National Center fo Coring and Drilling (C2FN) thanks to the national excellence equipment project CLIMCOR. It aims at gathering on a unique website the locations and metadata of any scientific coring/drilling performed by French teams or using French facilities, whatever the type of archive it is (lake/marine sediment; ice etc.). It uses international standard, notably IGSN (for samples), ORCID (for persons) and DOI (for campaigns). NCC follows the INSPIRE ISO 19115 standard in order to catalogue the data. For continental sediment, NCCR may be fed directly on the field through a specifically developed mobile application.Based on NCCR, further initiatives may be led. In particular, under the umbrella of LTER-France (Long Term Ecological Research), we developed ROZA in order to facilitate the re-use of data and samples. Here the idea is to capitalise the knowledge on a given lake from which several sediment cores can be taken through time. In that aim we selected at least one lake from each of the 13 areas composing the network LTER-France. To enter the database, a set of mandatory data must be provided under a pre-determined format. In that case, the insertion of ROZA within the network LTER will favor to use of paleodata by non-paleodata scientists, in particular ecologists.

In vitro cardiac differentiation of human pluripotent stem cells (hPSCs) closely recapitulates in vivo embryonic heart development and therefore, provides an excellent model to study human cardiac development. We recently generated the dual cardiac fluorescent reporter MESP1mCherry/wNKX2-5eGFP/w line in human embryonic stem cells (hESCs), allowing the visualization of pre-cardiac MESP1+ mesoderm and their further commitment towards the cardiac lineage, marked by activation of the cardiac transcription factor NKX2-5. Here, we performed a comprehensive whole genome based transcriptome analysis of MESP1-mCherry derived cardiac-committed cells. In addition to previously described cardiac-inducing signalling pathways, we identified novel transcriptional and signalling networks indicated by transient activation and interactive network analysis. Furthermore, we found a highly dynamic regulation of extracellular matrix components, suggesting the importance to create a versatile niche, adjusting to various stages of cardiac differentiation. Finally, we identified cell surface markers for cardiac progenitors, such as the Leucine-rich repeat-containing G-protein coupled receptor 4 (LGR4), belonging to the same subfamily of LGR5 and LGR6, established tissue/cancer stem cells markers. We provide a comprehensive gene expression analysis of cardiac derivatives from pre-cardiac MESP1-progenitors that will contribute to a better understanding of the key regulators, pathways and markers involved in human cardiac differentiation and development.

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype–phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx–p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows. mygrid.org.uk/repository/myGrid/PaulFisher/.

Life Science research has extended beyond in vivo and in vitro bench-bound science to incorporate in silico knowledge discovery, using resources that have been developed over time by different teams for different purposes and in different forms. The myGrid project has developed a set of software components and a workbench, Taverna, for building, running and sharing workflows that link third party bioinformatics services, such as databases, analytic tools and applications. Intelligently discovering prior services, workflow or data is aided by a Semantic Web of annotations, as is the building of the workflows themselves. Metadata associated with the workflow experiments, the provenance of the data outcomes and the record of the experimental process need to be flexible and extensible. Semantic Web metadata technologies would seem to be well-suited to building a Semantic Web of provenance. We have the potential to integrate and aggregate workflow outcomes, and reason over provenance logs to identify new experimental insights, and to build and export a Semantic Web of experiments that contributes to Knowledge Discovery for Taverna users and for the scientific community as a whole.

We show how state-of-the-art Semantic Web technology can be used in e-Science, in particular, to automate the classification of proteins in biology. We show that the resulting classification was of comparable quality to that performed by a human expert, and how investigations using the classified data even resulted in the discovery of significant information that had previously been overlooked, leading to the identification of a possible drug-target.

PhosphaBase is an ontology-driven database resource containing information on the protein phosphatase family. It is the first public resource dedicated to protein phosphatases, which are enzymes that perform dephosphorylation reactions. In conjunction with the phosphorylation action of protein kinases, phosphatases are involved in important control and communication mechanisms in the cell. They have also been implicated in many human diseases, including diabetes and obesity, cancers, and neurodegenerative conditions. PhosphaBase aims to centralize the growing base of knowledge in the phosphatase research domain. The resource is built around a formal, domain-specific DAML+OIL ontology, and the data are collected from heterogeneous biological sources using Gene Ontology terms as a means of data extraction. The overall ontology-driven architecture provides a robust structure with distinct advantages for sustainability and provides the potential for the development of diagnostic tools, as well as a data repository.