Critical assessment of high-throughput standalone methods for secondary structure prediction

By Hua Zhang, Tuo Zhang, Ke Chen, Kanaka Durga Kedarisetti, Marcin J. Mizianty, Qingbo Bao, Wojciech Stach, Lukasz Kurgan. 2011

54 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Lukasz Kurgan
2. Ronan M. Keegan
3. Quan Le
4. Mirko Torrisi
5. Ke Chen
6. Daniel J. Rigden
7. Jaclyn Bibby
8. Gianluca Pollastri

In Molecular Biology

1. Lukasz Kurgan
2. Ke Chen
3. Quan Le
4. Mirko Torrisi
5. Ronan M. Keegan
6. Daniel J. Rigden
7. Jaclyn Bibby
8. Gianluca Pollastri

Citations in the corpus

• Computational prediction of secondary and supersecondary structures. (2013, methodology): “Two recent comparative studies (16, 39) and prior evaluations within the framework of the EVA initiative show that SABLE achieves accuracy of about 78%.”
• Deep learning methods in protein structure prediction (2020, methodology): “A set of surveys of recent methods for the prediction of protein secondary structure can be found in [82–85] and a thorough comparative assessment of high-throughput predictors in [86].”
• AMPLE: a cluster-and-truncate approach to solve the crystal structures of small proteins using rapidly computed ab initio models. (2012, methodology): “PSIPRED is one of the most accurate available methods although, as with most methods, helices are predicted somewhat more accurately than strands (Zhang et al., 2011).”