A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction
By Yumeng Liu, Xiaolong Wang, B. Liu. 2019
144 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, 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 Computational Theory and Mathematics
- Damiano Piovesan
- Kuldip K. Paliwal
- Silvio C. E. Tosatto
- Jack Hanson
- Yaoqi Zhou
- Liam J. McGuffin
- Torsten Schwede
- Yang Zhang
In Molecular Biology
- Kuldip K. Paliwal
- Jack Hanson
- Yaoqi Zhou
- Thomas Litfin
- Damiano Piovesan
- Silvio C. E. Tosatto
- Yuedong Yang
- Jinbo Xu
Citations in the corpus
- Critical assessment of protein intrinsic disorder prediction (CAID) ‐ Results of round 2 (2023, background): “6 Some methods predict disordered regions derived from missing residues in X-ray experiments, while others attempt to capture context-dependent behavior (ID flavors), such as binding sites within IDRs.”
- SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, background): “A recent review by Liu et al. [21] placed SPOT-Disorder and AUCpreD as the two top-performing predictors for protein disorder prediction.”