The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies

2015 ◽  
Author(s):  
Volkan Uslan ◽  
Huseyin Seker
Keyword(s):  
Support Vector Regression ◽  
Peptide Binding ◽  
Quantitative Prediction ◽  
Support Vector ◽  
Binding Affinities

scholarly journals A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Valerie A. Walshe ◽  
Channa K. Hattotuwagama ◽  
Irini A. Doytchinova ◽  
Darren R. Flower
Keyword(s):  
T Cell ◽  
Peptide Binding ◽  
Data Driven ◽  
Quantitative Prediction ◽  
Binding Affinities ◽  
T Cell Epitopes ◽  
Histocompatibility Complex ◽  
Binding Peptides ◽  
Using Data ◽  
Binding Level

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as −log10 of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
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