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Daniel W. A. Buchan

Researcher at University College London

Publications -  23
Citations -  7775

Daniel W. A. Buchan is an academic researcher from University College London. The author has contributed to research in topics: Structural genomics & Structural Classification of Proteins database. The author has an hindex of 20, co-authored 23 publications receiving 6661 citations. Previous affiliations of Daniel W. A. Buchan include Imperial College London.

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The tomato genome sequence provides insights into fleshy fruit evolution

Shusei Sato, +323 more
- 31 May 2012 - 
TL;DR: A high-quality genome sequence of domesticated tomato is presented, a draft sequence of its closest wild relative, Solanum pimpinellifolium, is compared, and the two tomato genomes are compared to each other and to the potato genome.
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Scalable web services for the PSIPRED Protein Analysis Workbench

TL;DR: The PSIPRED Protein Analysis Workbench unites all of the previously available analysis methods into a single web-based framework and provides a greatly streamlined user interface with a number of new features to allow users to better explore their results.
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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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The PSIPRED Protein Analysis Workbench: 20 years on.

TL;DR: The work to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 years is presented and updates to some of the key predictive algorithms available through the website are surveyed.
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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.