Author

Tamás Szaniszló

Eötvös Loránd UniversityORCID 0000-0002-3130-9284Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

Mátyás PajkosiD· 2 shared Zsuzsanna DosztányiiD· 2 shared A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared Anastasia ChasapiiD· 1 shared András HatosiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMolecular Biology

Mátyás PajkosORCID

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2 papers

Recent·Most cited

Entering new circles: Expansion of the LC8/DYNLL1 interactome in the ciliary-centrosomal network through system-driven motif evolution

Mátyás Pajkos, Tamás Szaniszló, Máté Fülöp, Zsuzsanna Dosztányi

The paper identifies 57 high-confidence LC8 partners in the ciliary-centrosomal system, revealing motif evolution and expansion of the LC8 interactome.

bioRxiv · Sep 15, 2023 · PreprintMolecular Biology

Critical Assessment of Protein Intrinsic Disorder Prediction

Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, , Silvio C. E. Tosatto

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Nature Methods · Apr 19, 2021Peer-reviewedBioinformatics