Related references
Note: Only part of the references are listed.SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
Jaspreet Singh et al.
BIOINFORMATICS (2022)
PIPENN: protein interface prediction from sequence with an ensemble of neural nets
Bas Stringer et al.
BIOINFORMATICS (2022)
EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction
Sazan Mahbub et al.
BRIEFINGS IN BIOINFORMATICS (2022)
SignalP 6.0 predicts all five types of signal peptides using protein language models
Felix Teufel et al.
NATURE BIOTECHNOLOGY (2022)
Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms
Shiwei Li et al.
CURRENT OPINION IN STRUCTURAL BIOLOGY (2022)
Machine learning solutions for predicting protein-protein interactions
Rita Casadio et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2022)
PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention
SeyedMohsen Hosseini et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2022)
DELPHI: accurate deep ensemble model for protein interaction sites prediction
Yiwei Li et al.
BIOINFORMATICS (2021)
Embeddings from deep learning transfer GO annotations beyond homology
Maria Littmann et al.
SCIENTIFIC REPORTS (2021)
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)
Learning the protein language: Evolution, structure, and function
Tristan Bepler et al.
CELL SYSTEMS (2021)
A framework for understanding the functions of biomolecular condensates across scales
Andrew S. Lyon et al.
NATURE REVIEWS MOLECULAR CELL BIOLOGY (2021)
Protein-protein interaction site prediction through combining local and global features with deep neural networks
Min Zeng et al.
BIOINFORMATICS (2020)
ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence
Jiajun Qiu et al.
JOURNAL OF MOLECULAR BIOLOGY (2020)
SeRenDIP: SEquential REmasteriNg to DerIve profiles for fast and accurate predictions of PPI interface positions
Qingzhen Hou et al.
BIOINFORMATICS (2019)
SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences
Jian Zhang et al.
BIOINFORMATICS (2019)
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Martin Steinegger et al.
NATURE METHODS (2019)
HH-suite3 for fast remote homology detection and deep protein annotation
Martin Steinegger et al.
BMC BIOINFORMATICS (2019)
Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network
Buzhong Zhang et al.
NEUROCOMPUTING (2019)
Modeling aspects of the language of life through transfer-learning protein sequences
Michael Heinzinger et al.
BMC BIOINFORMATICS (2019)
SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
Jose M. Dana et al.
NUCLEIC ACIDS RESEARCH (2019)
Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains
Jian Zhang et al.
BRIEFINGS IN BIOINFORMATICS (2019)
Review and comparative assessment of sequence-based predictors of protein-binding residues
Jian Zhang et al.
BRIEFINGS IN BIOINFORMATICS (2018)
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Milot Mirdita et al.
NUCLEIC ACIDS RESEARCH (2017)
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger et al.
NATURE BIOTECHNOLOGY (2017)
ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model
Castrense Savojardo et al.
BIOINFORMATICS (2017)
Seeing the trees through the forest: sequence-based homo- and heteromeric protein-protein interaction sites prediction using random forest
Qingzhen Hou et al.
BIOINFORMATICS (2017)
Protein-protein interaction sites prediction by ensembling SVM and sample-weighted random forests
Zhi-Sen Wei et al.
NEUROCOMPUTING (2016)
Sequence specificity between interacting and non-interacting homologs identifies interface residues - a homodimer and monomer use case
Qingzhen Hou et al.
BMC BIOINFORMATICS (2015)
A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites
Zhi-Sen Wei et al.
IEEE TRANSACTIONS ON NANOBIOSCIENCE (2015)
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Baris E. Suzek et al.
BIOINFORMATICS (2015)
Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier
Kaustubh Dhole et al.
JOURNAL OF THEORETICAL BIOLOGY (2014)
BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions
Jianyi Yang et al.
NUCLEIC ACIDS RESEARCH (2013)
Applying the Naive Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites
Yoichi Murakami et al.
BIOINFORMATICS (2010)
Progress and challenges in predicting protein-protein interaction sites
Lakes Ezkurdia et al.
BRIEFINGS IN BIOINFORMATICS (2009)
Predicted protein-protein interaction sites from local sequence information
Y Ofran et al.
FEBS LETTERS (2003)
Share Paper
