MobiDB‐lite: fast and highly specific consensus prediction of intrinsic disorder in proteins
By Marco Necci, Damiano Piovesan, Zsuzsanna Dosztányi, Silvio C. E. Tosatto. 2017
178 citations recorded by the source corpus.
Subfields: Molecular Biology, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Zsuzsanna Dosztányi
- Gábor Erdös
- Tunca Dogan
- Alex Bateman
- Bálint Mészáros
- UniProt Consortium
- Burkhard Rost
- Rodrigo Lopez
In Spectroscopy
- Tunca Dogan
- Gábor Erdös
- Zsuzsanna Dosztányi
- Bálint Mészáros
- Alex Bateman
- Cathy H. Wu
- Hongzhan Huang
- Rodrigo Lopez
Citations in the corpus
- SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, neutral): “uk/downloads/DISOPRED/), MobiDB-lite [50], AUCpreD [18] (https://github.”
- IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, background): “However, integration into several resources, such as MobiDBlite (41), MobiDB 3.”
- UniProt: the universal protein knowledgebase in 2021 (2020, methodology): “The largest part of missing annotation seems to derive from intrinsically disordered (ID) protein regions, therefore we have collaborated with the MobiDB-lite resource to provide a consensus-based prediction of long disorder (27).”