# How to train your model

#### Legana Fingerhut

#### 2020-05-11

By default `ampir` predicts the probability of a protein to be an antimicrobial peptide (AMP) or not based on a trained SVM model with as input known AMP sequences corresponding to a wide diversity of organisms. However, within the `predict_amps` function there is a `model` argument that allows users to pass their own trained model object. Using a different trained model might be useful when users wish to e.g. use a taxonomic specific model to predict AMPs in a restricted group of taxa.

This vignette will go through a mock example of how you can train your own model using the `caret` package. For more information on how to use `caret` and the functions used within this example, please see the extensive documentation made by the author, Max Kuhn.

### Step 1: Obtain input data

First, a positive and negative dataset have to be obtained. In this example, we want to predict AMPs in bats and decide to train a model using protein sequences found in bats. The positive dataset are AMPs and the negative dataset are random sequences. Both datasets were obtained from UniProt:

- positive by using the search term keyword:antimicrobial taxonomy:“Chiroptera [9397]”
- negative by using the search term taxonomy:“Chiroptera [9397]”

For the positive dataset:

- read data
- add “positive” label
- remove non standard amino acids

    library(ampir)

    bat_pos <- read_faa(system.file("extdata/bat_positive.fasta.gz", package = "ampir"))
    bat_pos$Label <- "Positive"
    bat_pos <- remove_nonstandard_aa(bat_pos)

For the negative dataset:

- read data
- add “negative” lavel
- remove non standard amino acids
- remove sequences (if any) that are also present in the positive dataset
- randomly select the same number of sequences that are in the positive dataset

    bat_neg <- read_faa(system.file("extdata/bat_negative.fasta.gz", package = "ampir"))
    bat_neg$Label <- "Negative"
    bat_neg <- remove_nonstandard_aa(bat_neg)
    bat_neg <- bat_neg[!bat_neg$seq_aa %in% bat_pos$seq_aa,]
    bat_neg <- bat_neg[sample(nrow(bat_neg),78),]

Combine the positive and negative dataset

    bats <- rbind(bat_pos, bat_neg)

Calculate features on the combined positive and negative dataset and add the label column

    bats_features <- calculate_features(bats)
    bats_features$Label <- as.factor(bats$Label)
    rownames(bats_features) <- NULL

    library(caret)

Split feature set data and create train and test set with `caret`

    trainIndex <-createDataPartition(y=bats_features$Label, p=.7, list = FALSE)
    bats_featuresTrain <-bats_features[trainIndex,]
    bats_featuresTest <-bats_features[-trainIndex,]

Resample method using repeated cross validation and adding in a probability calculation with `caret`

    trctrl_prob <- trainControl(method = "repeatedcv", number = 10, repeats = 3,
                                classProbs = TRUE)

Train model using a support vector machine with radial kernel with `caret`. Note: Other classification models are supported too. For example, to use a random forest model in `caret`, `method` could be changed from “svmRadial” to “ranger”.

    my_bat_svm_model <- train(Label~.,
                           data = bats_featuresTrain[,-1], # excluding seq_name column
                           method="svmRadial",
                           trControl = trctrl_prob,
                           preProcess = c("center", "scale"))

Test model to get an indication of how well the model performs on test data with `caret`

    my_bat_pred <- predict(my_bat_svm_model, bats_featuresTest)
    cm <- confusionMatrix(my_bat_pred, bats_featuresTest$Label, positive = "Positive")

Subset from `cm$byClass`

Balanced Accuracy | Precision | Recall | F1
--- | --- | --- | ---
0.98 | 1 | 0.96 | 0.98

Convert the bat feature test data to the original FASTA type format containing just the sequence name and sequence as this is the required input data for `ampir`

    bat_test_set <- bats[bats$seq_name %in% bats_featuresTest$seq_name,][,-3]
    rownames(bat_test_set) <- NULL

Use the trained bat model in `ampir`’s `predict_amps` function on the bat test set

    my_bat_AMPs <- predict_amps(bat_test_set, min_len = 5, model = my_bat_svm_model)

`my_bat_AMPs` sample

| seq_name | seq_aa | prob_AMP
--- | --- | --- | ---
1 | G1P1T7… | MKALLTLGLL… | 0.999
2 | G1NZQ6… | MKLFLILIIL… | 0.986
3 | U3KZW3… | MKSLLILGLL… | 0.998
44 | P14392… | VHLSGEEKAA… | 0.010
45 | Q7I5M1… | MALIYTNTLL… | 0.014
46 | B2KI57… | MEPFSSKSLA… | 0.050