10 天前 - … learning algorithms have shown major promise in the field of molecular modeling,
due to their ability to analyze and learn … the power of deep learning tools toward predictive …

19 天前 - … MHC-peptide binding data has boosted machine learning-… The availability of extensive
MHC-peptide binding data … in machine learning-based approaches for predicting binding …

33 天前 - … learning has improved the identification of peptides that … of peptide v being displayed
is termed a peptide-MHC hit, … Different MHC alleles have different peptide binding prop…

36 天前 - … For the MLP, we specified a learning rate 1e-4 and a weight decay 1e-5,
employing a binary cross-entropy loss function. We are basing the name of MLPs on the "embedding …

37 天前 - … with additional peptides. For more details, we refer to the methods of the machine
learning based method used to predict the TCR-pMHC binding probability of TCR1, TCR2, …

66 天前 - … post-translationally modified MHCassociated peptides, remains limited … learning
de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides …

78 天前 - … deep learning protein design methods, ProteinMPNN and ESM-IF, in designing
fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through …

107 天前 - … In one of our previous studies, we aimed to combat this issue by integrating
multiple deep learning tools into a machine learning-based multivalent vaccine design called …

107 天前 - … We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding
affinity. … learning and semi-supervised learning methods into the cutting-edge deep learning …

107 天前 - … , we use it to predict the peptide binding of our test allele-clustered dataset. The
trained network takes a pMHC 3D model as input, masks each peptide residue and predicts the …