35 citations · 44 across the 6 of their papers we have counts for
6 papers
Protein Structure Prediction until CASP15
Arne Elofsson
In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational met…
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?
Elodie Laine, Stephan Eismann, Arne Elofsson +1
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has b…
Using micro- and macro-level network metrics unveils top communicative gene modules in psoriasis
Reyhaneh Naderi, Homa Saadati Mollaei, Arne Elofsson +1
Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin with significant morbidity, characterized by hyper proliferation of the epidermis. Even though p…
Deep transfer learning in the assessment of the quality of protein models
David Menéndez Hurtado, Karolis Uziela, Arne Elofsson
MOTIVATION: Proteins fold into complex structures that are crucial for their biological functions. Experimental determination of protein structures is costly and therefore limited…
ProQ3D: Improved model quality assessments using Deep Learning
Karolis Uziela, David Menéndez Hurtado, Björn Wallner +1
Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimi…
ProQ3: Improved model quality assessments using Rosetta energy terms
Karolis Uziela, Björn Wallner, Arne Elofsson
Motivation: To assess the quality of a protein model, i.e. to estimate how close it is to its native structure, using no other information than the structure of the model has been…