Biomedical subjects

Carole Goble

Publications and source records attributed to Carole Goble.

6 recordsLinked to original sources

PubMed2026

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

Artificial Intelligence↗

PubMed2006

Taverna: a tool for building and running workflows of services.

Taverna is an application that eases the use and integration of the growing number of molecular biology tools and databases available on the web, especially web services. It allows bioinformaticians to construct workflows or pipelines of services to perform a range of different analyses, such as sequence analysis and genome annotation. These high-level workflows can integrate many different resources into a single analysis. Taverna is available freely under the terms of the GNU Lesser General Public License (LGPL) from http://taverna.sourceforge.net/.

Computational Biology↗

PubMed2005

A linkable identity privacy algorithm for HealthGrid.

The issues of confidentiality and privacy have become increasingly important as Grid technology is being adopted in public sectors such as healthcare. This paper discusses the importance of protecting the confidentiality and privacy of patient health/medical records, and the challenges exhibited in enforcing this protection in a Grid environment. It proposes a novel algorithm to allow traceable/linkable identity privacy in dealing with de-identified medical records. Using the algorithm, de-identified health records associated to the same patient but generated by different healthcare providers are given different pseudonyms. However, these pseudonymised records of the same patient can still be linked by a trusted entity such as the NHS trust or HealthGrid manager. The paper has also recommended a security architecture that integrates the proposed algorithm with other data security measures needed to achieve the desired security and privacy in the HealthGrid context.

Algorithms↗

PubMed2003

Sentinel: towards an ambient mobility network.

PURPOSE: We are concerned with aiding the mobility of visually impaired travellers around often complex and unfamiliar internal and urban environments. To do this we focus on a users interaction with ambient devices because these device types provide an easy entry point for visually impaired individuals to interact with their surroundings. By augmenting the physical environment with mobility focused ambient devices and making existing devices universally accessible our goal of easy, focused, and confident mobility can be achieved. METHOD: We identify, through paper reviews and studies of empirical and anecdotal evidence, the social and technical problems that have so far barred consistent and cohesive development of an ambient mobility-network. RESULTS: We suggest that multi-model sensory-interaction with objects and assistive devices within an environment is the only way to accomplish easy, focused, and confident mobility for visually impaired travellers. We find that universal access to ambient devices does not exist when the user interface and the device functionality are conjoined. And we also suggest that this universal access will not occur if there are only benefits for one small minority user group. CONCLUSIONS: We conclude that to assist mobility we need to provide universal access to objects and devices within the environment. Also, to support the mobility of visually impaired travellers we must first support all travellers regardless of their mobility needs. And to accomplish this we must separate the user interface from the real purpose (the functionality) of the object/device.

Electronics↗

PubMed2002

OILing the way to machine understandable bioinformatics resources.

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed, and heterogeneous resources. The web, as an enabler for interoperability, has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web [a machine understandable world-wide web (WWW)], the ontology inference layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bio-ontology using OIL.

Algorithms↗

PubMed2002

Building a bioinformatics ontology using OIL.

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.

Algorithms↗