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- W3082278482 abstract "Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range contacts could help topology-level structure modeling. Thus, contact prediction and contact-assisted protein folding also proves the importance of this problem. In this thesis, I will briefly introduce the extant related work, then show how to establish the contact prediction through unsupervised graphical models with topology constraints. Further, I will explain how to use the supervised deep learning methods to further boost the accuracy of contact prediction. Finally, I will propose a scoring system called diversity score to measure the novelty of contact predictions, as well as an algorithm that predicts contacts with respect to the new scoring system." @default.
- W3082278482 created "2020-09-08" @default.
- W3082278482 creator A5023777406 @default.
- W3082278482 date "2020-08-31" @default.
- W3082278482 modified "2023-10-18" @default.
- W3082278482 title "Unsupervised and Supervised Structure Learning for Protein Contact Prediction" @default.
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- W3082278482 doi "https://doi.org/10.48550/arxiv.2009.00133" @default.
- W3082278482 hasPublicationYear "2020" @default.
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