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Domenico Cozzetto

Researcher at University College London

Publications -  61
Citations -  5052

Domenico Cozzetto is an academic researcher from University College London. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 24, co-authored 37 publications receiving 4233 citations. Previous affiliations of Domenico Cozzetto include Francis Crick Institute & Guy's and St Thomas' NHS Foundation Trust.

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A large-scale evaluation of computational protein function prediction

Predrag Radivojac, +107 more
- 01 Mar 2013 - 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
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PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.

TL;DR: A novel method, PSICOV, is presented, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction and displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks.
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DISOPRED3: precise disordered region predictions with annotated protein-binding activity

TL;DR: This work describes DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them and shows that this predictor generates precise assignments of disordered protein binding regions and that it compares well with other publicly available tools.
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An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Yuxiang Jiang, +156 more
- 07 Sep 2016 - 
TL;DR: The second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function, was conducted by as mentioned in this paper. But the results of the CAFA2 assessment are limited.
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The PMDB Protein Model Database.

TL;DR: The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins to provide access to models published in the scientific literature, together with validating experimental data.