Man pages for yujijun/neoantigenML
Machine Learning Methods for Neoantigen Filtering
| BenchmarkEva | Title Benchmark calculation In particular unique combination... |
| CorBetween | Title |
| CorPlot | Title |
| Feature_Calcualtion | Fragment feature calculation |
| FeatureSebyAuto | Title Automating the Feature Selection and benchmark... |
| FeatureSeByFilter | Title |
| FeatureSebyNestedresam | Title |
| FeatureSebyVariImpFil | Title Feature Selection by Embedded Filter Methods |
| FeatureWrapperMethod | Title Feature Selection by Wrapper Methods Use this function... |
| Neodataset | formated neoantigens dataset for machine learning running |
| neoML.all | Title glmnet/kknn/range/svm/xgboost training model |
| neoML.rpart | Title Classify by classif.rpart |
| PepFeatureDesc | Title peptide Feature Description |
| PepFeatureDesc.mutate | Title peptide Feature Description |
| PepFrag | Create fragment of peptide set |
| PepFragSign | Title PepFraSign |
| pepFragVis | Title visualization for fragment of peptides |
| peptides | positive and negative neopeptides from netMHCpan |
| pepVisLength | Visualization of length statistics in peptides. |
| PerformanceEva | Title Performance evaluation of single machine learning... |
| Pointbiserial | Title |
| PropertyofPepSingle | Title Properties of whole peptides |
