State of the art and open challenges in community-driven knowledge curation

Developing organizational learning for scaling-up community-based primary health care in Ghana

INTRODUCTION

Achieving effective community-based primary health care requires evidence for guiding strategic decisions that must be made. However, research processes often limit data collection to particular organizational levels or disseminate results to specific audiences. Decision-making that emerges can fail to account for the contrasting perspectives and needs of managers at each organizational level. The Ghana Health Service (GHS) addressed this problem with a multilevel and sequential research and action approach that has provided two decades of implementation learning for guiding community-based primary health care development.

METHOD

The GHS implementation research initiatives progressed from (i) a participatory pilot investigation to (ii) an experimental trial of strategies that emerged to (iii) replication research for testing scale-up, culminating in (iv) evidence-based scale-up of a national community-based primary health care program. A reform process subsequently repeated this sequence in a manner that involved stakeholders at the community, sub-district, district, and regional levels of the system. The conduct, interpretation, and dissemination of results that emerged comprised a strategy for achieving systems learning by conducting investigations in phases in conjunction with bottom-up knowledge capture, lateral exchanges for fostering peer learning at each system level, and top-down processes for communicating results as policy. Continuous accumulation of qualitative data on stakeholder reactions to operations at each organizational level was conducted in conjunction with quantitative monitoring of field operations.

RESULTS

Implementation policies were enhanced by results associated with each phase. A quasi-experiment for testing the reform process showed that scale-up of community-based primary health care was accelerated, leading to improvements in childhood survival and reduced fertility.

CONCLUSION

Challenges to system learning were overcome despite severe resource constraints. The integration of knowledge generation with ongoing management processes institutionalized learning for achieving evidence-driven program action.

PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS

Motivation

We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information.

Results

To demonstrate PRINCESS' performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster).

Availability and Implementation

https://github.com/achenfengb/PRINCESS_opensource.

Contact

shw070@ucsd.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Modelling expertise at different levels of granularity using semantic similarity measures in the context of collaborative knowledge-curation platforms

Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements - or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP.

Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions

Background

A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications.

Results

Here we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods.

Conclusion

REX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-015-0020-6) contains supplementary material, which is available to authorized users.

Capturing domain knowledge from multiple sources: the rare bone disorders use case

Background

Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. The community-driven ontology curation process, however, ignores the possibility of multiple communities building, in parallel, conceptualisations of the same domain, and thus providing slightly different perspectives on the same knowledge. The individual nature of this effort leads to the need of a mechanism to enable us to create an overarching and comprehensive overview of the different perspectives on the domain knowledge.

Results

We introduce an approach that enables the loose integration of knowledge emerging from diverse sources under a single coherent interoperable resource. To accurately track the original knowledge statements, we record the provenance at very granular levels. We exemplify the approach in the rare bone disorders domain by proposing the Rare Bone Disorders Ontology (RBDO). Using RBDO, researchers are able to answer queries, such as: “What phenotypes describe a particular disorder and are common to all sources?” or to understand similarities between disorders based on divergent groupings (classifications) provided by the underlying sources.

Availability

RBDO is available at http://purl.org/skeletome/rbdo. In order to support lightweight query and integration, the knowledge captured by RBDO has also been made available as a SPARQL Endpoint at http://bio-lark.org/se_skeldys.html.

Discovering beaten paths in collaborative ontology-engineering projects using Markov chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.

On Crowd-verification of Biological Networks

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks.

Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified.

The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

Community intelligence in knowledge curation: an application to managing scientific nomenclature

Harnessing community intelligence in knowledge curation bears significant promise in dealing with communication and education in the flood of scientific knowledge. As knowledge is accumulated at ever-faster rates, scientific nomenclature, a particular kind of knowledge, is concurrently generated in all kinds of fields. Since nomenclature is a system of terms used to name things in a particular discipline, accurate translation of scientific nomenclature in different languages is of critical importance, not only for communications and collaborations with English-speaking people, but also for knowledge dissemination among people in the non-English-speaking world, particularly young students and researchers. However, it lacks of accuracy and standardization when translating scientific nomenclature from English to other languages, especially for those languages that do not belong to the same language family as English. To address this issue, here we propose for the first time the application of community intelligence in scientific nomenclature management, namely, harnessing collective intelligence for translation of scientific nomenclature from English to other languages. As community intelligence applied to knowledge curation is primarily aided by wiki and Chinese is the native language for about one-fifth of the world’s population, we put the proposed application into practice, by developing a wiki-based English-to-Chinese Scientific Nomenclature Dictionary (ESND; http://esnd.big.ac.cn). ESND is a wiki-based, publicly editable and open-content platform, exploiting the whole power of the scientific community in collectively and collaboratively managing scientific nomenclature. Based on community curation, ESND is capable of achieving accurate, standard, and comprehensive scientific nomenclature, demonstrating a valuable application of community intelligence in knowledge curation.