In a major scientific breakthrough, the latest version of #AlphaFold has been recognised as a solution to one of biology's grand challenges - the “protein folding problem”. It was validated today at #CASP14, the biennial Critical Assessment of protein Structure Prediction (1/3)
CASP is both the gold standard for assessing predictive techniques and a unique global community built on shared endeavour. Accuracy is measured on a range of 0-100 “GDT”. #AlphaFold has a median score of 92.4 GDT across all targets - its average error about the width of an atom.
We’re excited about the potential impact #AlphaFold may have on the future of biological research and scientific discovery. Thank you to the CASP organisers & the whole community - we look forward to the many years of hard work and discovery ahead: bit.ly/3qdko1Q
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Yesterday we shared the news that #AlphaFold has been recognised as a solution to the ‘protein folding problem’ by #CASP14, the biennial Critical Assessment of Protein Structure Prediction. But what exactly is protein folding, and why is it important? A thread… (1/6)
Proteins are the building blocks of life - they underpin the biological processes in every living thing. If you could unravel a protein you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. (2/6)
Interactions between these amino acids make the protein fold, as it finds its shape out of almost limitless possibilities. For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. (3/6)
He suggests the Deep Learning Book from @mitpress for a comprehensive introduction to the fundamentals of DL: bit.ly/351qMzb (1/7)
Overwhelmed with the number of available machine learning courses? @seb_ruder recommends taking a look through @venturidb’s curated - and ranked - list available on @freeCodeCamp.
Are you a beginner looking for a lesson on the Monte Carlo method?
Taylan’s own, “A Tutorial Introduction to Monte Carlo methods, Markov Chain Monte Carlo and Particle Filtering” is available here: bit.ly/3cAQ8XG#AtHomeWithAI
Looking for something challenging and fun? @KerenGu suggests Project Euler, a series of complex mathematical/computer programming problems hosted in a fun and recreational context. Test your skills and play along here: bit.ly/3bxBmAj#AtHomeWithAI (3/5)
Looking to learn more about AI? Our researchers are continuing to share their #AtHomeWithAI recommendations!
Today’s choices come from William Isaac (@wsisaac), a senior research scientist who specialises in ethics, bias and fairness. (1/5)
For an overview on fairness & how it applies to machine learning, William suggests diving into this freely available book [long read] by @s010n@mrtz and @random_walker!
@random_walker also discusses the various definitions of fairness and the tradeoffs they present for society in the video tutorial “21 definitions of fairness and their politics”