Collaboration to reduce antibiotic use and resistance and identify opportunities for improvement and awareness (CARA).

Introduction: CARA is a five-year Health Research Board (HRB) project. Superbugs cause resistant infections that are difficult to treat and pose a serious threat to human health. Providing tools to explore the prescription of antibiotics by GPs may help identify gaps where improvements can be made.

Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding.

The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction.

Corona citizens' science project-repeated surveys of the Irish response to COVID-19 and subsequent lockdown and restrictive measures.

Background: Worldwide, many people have been affected by COVID-19, a novel respiratory illness, caused by a new type of coronavirus SARS-CoV2. The COVID-19 outbreak is considered a pandemic and has created a number of challenges for the general population, patients, and healthcare professionals. Lockdowns have been implemented to slow down the spread of the virus with the expectation that these restrictions will limit the number of cases, and hence the number of hospitalizations and ICU admissions.

Synergy Between Embedding and Protein Functional Association Networks for Drug Label Prediction Using Harmonic Function.

Semi-Supervised Learning (SSL)is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage of unlabeled data. For instance, to identify drugs and targets where a few genes are known to be associated with a specific target for drugs and considered as labeled data.

Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.

Identifying the unintended effects of drugs (side effects) is a very important issue in pharmacological studies. The laboratory verification of associations between drugs and side effects requires costly, time-intensive research. Thus, an approach to predicting drug side effects based on known side effects, using a computational model, is highly desirable.

Linked data and online classifications to organise mined patterns in patient data.

In this paper, we investigate the use of web data resources in medicine, especially through medical classifications made available using the principles of Linked Data, to support the interpretation of patterns mined from patient care trajectories. Interpreting such patterns is naturally a challenge for an analyst, as it requires going through large amounts of results and access to sufficient background knowledge. We employ linked data, especially as exposed through the BioPortal system, to create a navigation structure within the patterns obtained form sequential pattern mining.

Where to Publish and Find Ontologies? A Survey of Ontology Libraries.

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to "talk" to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies.

Decision support and knowledge management in oncology using hierarchical classification.

This paper presents the KASIMIR research project for the management of decision protocols in oncology. A decision protocol is a kind of decision tree implemented in an object-based representation formalism. A reasoner based on such a formalism and on hierarchical classification is coupled with a knowledge editor.