Thomas Litfin
Orcid: 0000-0002-4863-3865
According to our database1,
Thomas Litfin
authored at least 16 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search.
Genom. Proteom. Bioinform., 2024
2023
CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins.
Nucleic Acids Res., July, 2023
2022
Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.
Bioinform., 2022
SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.
Bioinform., 2022
Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives.
Briefings Bioinform., 2022
2021
RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis.
Bioinform., 2021
Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning.
Bioinform., 2021
SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.
Bioinform., 2021
2020
SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map.
J. Comput. Chem., 2020
Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning.
J. Comput. Biol., 2020
Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning.
Bioinform., 2020
2019
SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites.
J. Chem. Inf. Model., 2019
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning.
Genom. Proteom. Bioinform., 2019
Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.
Bioinform., 2019
2018
Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.
Bioinform., 2018
2017
SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library.
Bioinform., 2017