FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards.

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming.

Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.

Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them.

Bioimage analysis workflows: community resources to navigate through a complex ecosystem.

Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them.  One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows.

biotoolsSchema: a formalized schema for bioinformatics software description.

Background: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources.

Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population.

Background And Purpose: The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks.

Methods: We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019.

Prediction of Unruptured Intracranial Aneurysm Evolution: The UCAN Project.

Background: Management of small (<7 mm) unruptured intracranial aneurysms (UIA) remains controversial. Retrospective studies have suggested that post gadolinium arterial wall enhancement (AWE) of UIA on magnetic resonance imaging (MRI) may reflect aneurysm wall instability, and hence may highlight a higher risk of UIA growth. This trial aims at exploring wall imaging findings of UIAs with consecutive follow-up to substantiate these assumptions.

Whole-exon sequencing of human myeloma cell lines shows mutations related to myeloma patients at relapse with major hits in the DNA regulation and repair pathways.

Background: Human myeloma cell lines (HMCLs) are widely used for their representation of primary myeloma cells because they cover patient diversity, although not fully. Their genetic background is mostly undiscovered, and no comprehensive study has ever been conducted in order to reveal those details.

Methods: We performed whole-exon sequencing of 33 HMCLs, which were established over the last 50 years in 12 laboratories.

Understanding the Pathophysiology of Intracranial Aneurysm: The ICAN Project.

Background: Understanding the pathophysiologic mechanism of intracranial aneurysm (IA) formation is a prerequisite to assess the potential risk of rupture. Nowadays, there are neither reliable biomarkers nor diagnostic tools to predict the formation or the evolution of IA. Increasing evidence suggests a genetic component of IA but genetics studies have failed to identify genetic variation causally related to IA.

OntoVIP: an ontology for the annotation of object models used for medical image simulation.

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit.

A virtual imaging platform for multi-modality medical image simulation.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project.

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services.

A distributed security policy for neuroradiology data sharing.

Grids are key technologies to federate data distributed in multiple neuroscience centers, thus enabling large scale multi-centric studies. However, the take up of these technologies is slow due to the difficulty to manipulate sensitive neuroradiological data in an open environment and the recognized risk of federated sites to loose control over their valuable data. In this paper we propose a distributed data access control policy, enabling the federation of existing data stores, where local security policies prevail, to supports multi-centric neuroscience studies.

NeuroLOG: a community-driven middleware design.

The NeuroLOG project designs an ambitious neurosciences middleware, gaining from many existing components and learning from past project experiences. It is targeting a focused application area and adopting a user-centric perspective to meet the neuroscientists expectations. It aims at fostering the adoption of HealthGrids in a pre-clinical community.