A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry ...

Data really powers everything that we do. Research activities in the data science area are concerned with the development of machine learning and computational statistical methods, their theoretical ...

Researchers developed a new computational method to analyze complex tissue data that could transform our current understanding of diseases and how we treat them. Researchers at the University of ...

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...

This course is designed for engineering graduate students who are interested in furthering their knowledge in advanced and emerging methods of engineering design, with the focus on computational ...

Our group focuses on understanding the genetic underpinnings of human health. We utilize large-scale biobanks (such as the UK Biobank and FinnGen) to investigate how genetic variation, including ...

This course is compulsory on the MSc in Financial Mathematics. This course is available on the MSc in Quantitative Methods for Risk Management, MSc in Statistics (Financial Statistics) and MSc in ...

Introduction to a wide range of computational techniques for engineering design. Modeling, simulation, optimization, design software, examples/projects with emphasis on computational techniques for ...

Researchers have developed a computational method that allows them to determine not if an entire imaging picture is accurate, but if any given point on the image is probable, based on the assumptions ...