Protein structure prediction with machine learning

Research-Outreach-Shuichiro-Makigaki

Shuichiro Makigaki and Dr Takashi Ishida, from the Department of Computer Science at Tokyo Institute of Technology, are developing a new sequence alignment generation model that employs machine learning and dynamic programming to predict protein structures. This novel methodology can also be applied to homology detection which is fundamental to bioinformatics. A protein’s function is dictated by its three-dimensional structure. If the structure is […]

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Reconstructing astronomical images with machine learning

Reconstructing astronomical images with machine learning

Much of what we know about how our universe works has been learnt by analysing the astronomical signals captured from the sky. However, these signals will inevitably have some noise associated with them – so how can astronomers be sure that their observations of strange, unexpected signals reflect reality? Edward Higson at the University of Cambridge and his colleagues have […]

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Biases from Big Data: the prejudiced computer

In this article Professor Nasraoui’s work focuses on Big Data. She examines how Machine Learning can lead to unreliable and biased models, problems around explainability and whether increased personalisation contributes to polarisation of opinions.

Big Data and Machine Learning seem to be the modern buzzword answers for every problem. Areas such as healthcare, fraud prevention and sales are just a few of the places that are thought to benefit from self-learning and improving machines that can be trained on huge datasets. However, how carefully do we scrutinise these algorithms and investigate possible biases that […]

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