SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks.
By Yuedong Yang, Rhys Heffernan, Kuldip K. Paliwal, James G. Lyons, Abdollah Dehzangi, Alok Sharma, Jihua Wang, Abdul Sattar, Yaoqi Zhou. 2017
149 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, 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
- Lisa N. Kinch
- Nick V. Grishin
- Jimin Pei
- Zbyszek Otwinowski
- Lauren L. Porter
- L. Looger
- Andriy Kryshtafovych
- Kazutaka Katoh
In Molecular Biology
- Lisa N. Kinch
- Nick V. Grishin
- Jimin Pei
- Zbyszek Otwinowski
- Lauren L. Porter
- L. Looger
- Andriy Kryshtafovych
- Kazutaka Katoh
Citations in the corpus
- Extant fold-switching proteins are widespread (2018, methodology): “Yang Y, et al. (2017) SPIDER2: A package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks.”
- Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “Prediction of 3-state secondary structures (helix, strand, and coil) were made by three programs (PSIPRED [94], SPIDER [95], and PSSpred [96]), resulting in nine features.”