Improved methods for predicting peptide binding affinity to MHC class II molecules

By Kamilla K Jensen, Massimo Andreatta, Paolo Marcatili, Søren Buus, J. Greenbaum, Zhen Yan, Alessandro Sette, Bjoern Peters, Morten Nielsen. Immunology, 2018

725 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9ee9eff66e12a693b2948cf7b8206d67e23d6f34

How researchers see this paper

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

In Immunology

1. Jure Leskovec
2. Kexin Huang
3. Yusuf H. Roohani

In Molecular Biology

1. Marinka Zitnik
2. Kexin Huang
3. Jure Leskovec
4. Cao Xiao
5. Jimeng Sun
6. Tianfan Fu
7. Yusuf H. Roohani
8. Connor W. Coley

In Radiology, Nuclear Medicine and Imaging

1. Jure Leskovec
2. Jimeng Sun
3. Marinka Zitnik

Citations in the corpus

• NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data (2020, neutral): “2 (23), MixMHC2pred (11), MHCnuggets (24) and DeepSeqPanII (25).”
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “TDC.MHC2_IEDB_Jensen: This MHC Class II data set was used to train the NetMHCIIpan (Jensen et al. 2018).”