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- W4380629398 abstract "Recent breakthroughs in protein structure prediction have increasingly relied on the use of deep neural networks. These recent methods are notable in that they produce 3-D atomic coordinates as a direct output of the networks, a feature which presents many advantages. Although most techniques of this type make use of multiple sequence alignments as their primary input, a new wave of methods have attempted to use just single sequences as the input. We discuss the make-up and operating principles of these models, and highlight new developments in these areas, as well as areas for future development." @default.
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- W4380629398 date "2023-08-01" @default.
- W4380629398 modified "2023-10-12" @default.
- W4380629398 title "Machine learning methods for predicting protein structure from single sequences" @default.
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- W4380629398 doi "https://doi.org/10.1016/j.sbi.2023.102627" @default.
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