NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

By E. Karosiene, Claus Lundegaard, Ole Lund, Morten Nielsen. Immunogenetics, 2011

384 citations recorded by the source corpus.

Subfields: Microbiology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b501f1feca1a363583cd395985c16198d3d78da6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Microbiology

1. Morten Nielsen
2. Massimo Andreatta
3. Bjoern Peters
4. Alessandro Sette
5. Peng Wang

In Molecular Biology

1. Morten Nielsen
2. Bjoern Peters
3. Massimo Andreatta
4. Alessandro Sette
5. Sinu Paul
6. Ole Lund
7. J. Greenbaum
8. Swapnil Mahajan

In Radiology, Nuclear Medicine and Imaging

1. Bjoern Peters
2. Morten Nielsen
3. Alessandro Sette
4. Massimo Andreatta
5. Sinu Paul
6. Claus Lundegaard
7. Ole Lund
8. Paolo Marcatili

Citations in the corpus

• The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “Next, a user can select a preferred method from a list including IEDB recommended, consensus (17), netMHCpan (10, 18), ANN (4, 19–22), scoring matrix method (SMM) (5), SMMPMBEC (23), Combinatorial Library (24), PickPocket (25), and netMHCcons (26).”
• Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “Because of this difference between the allele-specific and the pan-specific method, we implemented a simple combination of two methods as this has been shown to improve the predictive performance for MHC class I molecules (33).”
• Genomic features of response to combination immunotherapy in patients with advanced non-small cell lung cancer (2018, neutral): “…https://github.com/hammerlab/topiary/ ABSOLUTE Carter et al., 2012 (PMID 22544022) http://archive.broadinstitute.org/cancer/ cga/absolute NetMHCcons Karosiene et al., 2012 (PMID 22009319) http://www.cbs.dtu.dk/services/ NetMHCcons/ (Continued on next page) Cancer Cell 33, 843–852.e1–e4, May 14,…”
• Immune epitope database analysis resource (2012, methodology): “The combined dataset consisted of (cid:2) 1200 measurements with 20% composed of binders, and the average predictive performance on this dataset for the IEDB consensus prediction was 0.96 AUC, notably higher than our own prediction performance estimates, only surpassed by the NetMHCcons (10), NetMHC”
• NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets (2016, methodology): “Examples of the two types of prediction tools are (reflected by high performance in the IEDB weekly automated MHC class I benchmark [5]): allele-specific (NetMHC [6, 7], SMM [8, 9]) and pan-specific (NetMHCpan [2, 3], NetMHCcons [10]).”
• A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “…[28], NetMHCstabpan 1.0 [29], NetMHCPan 4.0 [30], MHCflurry 1.2.0 [31], MHCnuggets 2.0 [32], ConvMHC [33] and HLA-CNN [34]; and (iii) methods based on the integration of different peptide-binding predictors, including NetMHCcons 1.1 [35] and IEDB-analysisresource-consensus(IEDB-AR-Consensus)[25].”