Abstract
Artificial intelligence in the lab: ask not what your computer can do for you.
Highlights
In 1957, Herbert Simon, a pioneer of artificial intelligence, predicted that a computer would be the world chess champion within 10 years
Many bioinformatics algorithms under the hood rely on statistical models trained on such data to predict – often from nucleotide or amino acid sequences – the structure of genes, the function, location, domain content and secondary structure of proteins, the interactions of proteins with other proteins and DNA, phenotypes, etc
Search algorithms can iteratively try mutating a given sequence and keep those changes considered beneficial by a function predictor (Guimaraes et al, 2014), for example to improve protein production. In essence, such sequencedesign approaches are similar to the AlphaGo setup, in which deep learning networks are used to evaluate Go board positions and moves, based on which a search algorithm decides the best move to make
Summary
In 1957, Herbert Simon, a pioneer of artificial intelligence, predicted that a computer would be the world chess champion within 10 years. In essence, such sequence (re)design approaches are similar to the AlphaGo setup, in which deep learning networks are used to evaluate Go board positions and moves, based on which a search algorithm decides the best move to make.
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