Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
By Morten Nielsen, Claus Lundegaard, Peder Worning, Sanne Lise Lauemøller, Kasper Lamberth, Søren Buus, Søren Brunak, Ole Lund. 2003
380 citations recorded by the source corpus.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
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
- Morten Nielsen
- Bjoern Peters
- Alessandro Sette
- Massimo Andreatta
- Ole Lund
- J. Greenbaum
- Paolo Marcatili
- Claus Lundegaard
In Radiology, Nuclear Medicine and Imaging
- Bjoern Peters
- Morten Nielsen
- Alessandro Sette
- Massimo Andreatta
- Claus Lundegaard
- Marta Gomez-Perosanz
- J. L. Sánchez-Trincado
- Pedro A. Reche
Citations in the corpus
- Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “All data points are experimental IC50 binding values which were log-transformed to fall in the range between 0 and 1 using the relation 1−log(IC50nM)/log(50,000) as explained in (27).”
- Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, neutral): “…https://github.com/hubentu/MAC MuTect 1.1.4 Cibulskis et al., 2013; PMID: 23396013 http://archive.broadinstitute.org/cancer/cga/mutect NetMHC 4.0 Nielsen et al., 2003; PMID: 12717023 http://www.cbs.dtu.dk/services/NetMHC/ OncoSNP-SEQ Yau, 2013; PMID: 23926227…”
- Gapped sequence alignment using artificial neural networks: application to the MHC class I system (2016, background): “Contact: mniel@cbs.dtu.dk Supplementary information: Supplementary data are available at Bioinformatics online.”
- Immune epitope database analysis resource (IEDB-AR) (2008, background): “ANN, on the other hand, uses neural networks with diverse sequence encoding schemes to model-binding specificity (5).”
- NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007, methodology): “Artificial Neural Networks (ANN) are ideally suited to recognize non-linear patterns, which are believed to contribute to peptide-HLA-I interactions [15,16,21,22].”
- Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “Likewise, ANNs were also applied first to the prediction of peptide binding to MHC I [48, 49] and later to MHC II molecules [50] (Table 1).”