Our research team has recently performed an in-depth analysis of the current trends in artificial intelligence (AI) applied to brain age estimation. Leveraging advanced neuroimaging data from the UK Biobank (UKBB), a comprehensive dataset containing high-quality brain imaging data [ukb_web], and utilising state-of-the-art machine learning (ML) and deep learning (DL) techniques, we have developed robust brain clocks that show significant potential in predicting brain age and detecting early signs of neurodegenerative diseases. Read our latest publication in NeuroImage here!
Read MoreArtificial intelligence (AI) is transforming medical research, with deep learning models offering new possibilities in diagnostics and treatment planning. Deep learning has indeed achieved impressive results in several medical fields, including cancer detection, cardiovascular risk assessment, and brain imaging. These models excel at recognizing intricate patterns within data, often surpassing human experts. However, they frequently operate as "black boxes," generating accurate predictions without providing clear explanations. The lack of interpretability poses a serious obstacle to the adoption of deep learning models, especially in healthcare where transparency and trust can directly affect patient outcomes.
Read MoreAgeing research revolves around the challenges and opportunities posed by an older population with the aim of better understanding the ageing process across the lifespan. Longitudinal databases are crucial tools in this research as they enable the tracking of the same samples (e.g., the same individuals) at different time points and often - in the context of ageing - over a long period of time. This allows the observation of how variables change over time and the detection of trends that cannot be typically identified in cross-sectional studies.
Read MoreLongitudinal studies are essential for understanding how treatments impact individuals over time. These studies track changes, observe trends, and enable researchers to make informed decisions about the effectiveness of interventions. However, the challenge in analysing data from these studies arises from the repeated measurements often taken from the same subjects, which introduces complex correlations. Mixed Effects Models (MEMs) provide a sophisticated and practical solution for exploring these complexities and drawing meaningful conclusions about treatment effects.
Read MoreThe world of pharmaceutical research is being revolutionised by recent advances in computational techniques and computer power. Encompassing sophisticated computational methods to design and optimise new drug candidates, Computer-Aided Drug Design (CADD) lies at the core of this transformation and is becoming an integral part of how new drugs are discovered. Here at Oxcitas, we are using CADD to make drug development faster, more targeted, and more efficient.
Read MoreDrug development and discovery is a lengthy and costly process involving the systematic search for novel compounds with specific biological activity. Modern advances in computer power, algorithms and software development, as well as the availability of computing resources and their decreasing costs, have contributed to the development of in silico methods to support and aid drug discovery and development throughout the pipeline, with a particular impact on the early stages of drug discovery.
Read MoreThe world population is ageing. The current world population of 7.6 billion is expected to reach 8.6 billion by 2030, at which point an estimated 1 in 6 people will be aged 60 years and over. This demographic shift is pushing ageing research to the forefront, with a potentially huge impact on the lives of millions of people around the world.
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