Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study.

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR).

The role of the gut microbiome in the development of hepatobiliary cancers.

Hepatobiliary cancers, including hepatocellular carcinoma and cancers of the biliary tract, share high mortality and rising incidence rates. They may also share several risk factors related to unhealthy western-type dietary and lifestyle patterns as well as increasing body weights and rates of obesity. Recent data also suggest a role for the gut microbiome in the development of hepatobiliary cancer and other liver pathologies.

Genetically Predicted Circulating Concentrations of Micronutrients and COVID-19 Susceptibility and Severity: A Mendelian Randomization Study.

Background: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which since 2019 has caused over 5 million deaths to date. The pathogenicity of the virus is highly variable ranging from asymptomatic to fatal. Evidence from experimental and observational studies suggests that circulating micronutrients may affect COVID-19 outcomes.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures. Advances in data fusion and computational modeling have independently progressed towards addressing this issue.

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges.

Feature Representations for Neuromorphic Audio Spike Streams.

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal.

Real-time classification and sensor fusion with a spiking deep belief network.

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers.