HIGHLIGHTS
- who: Samantha Petti and colleagues from the NSF-Simons Center for the Mathematical and Statistical Analysis of Biology, University, have published the research: End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman, in the Journal: (JOURNAL)
- what: The authors implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. As a proof of concept the authors demonstrate that by connecting the differentiable alignment module to AlphaFold2 and maximizing predicted confidence the . . .
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