README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/",
  out.width = "100%"
)
```

# The dynamic landscape of peptide function prediction

This curated list collects peer-reviewed machine learning models dedicated to predicting specific properties of peptides. 
For an interactive and browse-able list, please visit: https://biogenies.info/peptide-prediction-list/.

```{r, echo=FALSE,message=FALSE,results='asis'}
library(glue)
library(magrittr)

write_entry <- function(x) {
  res <- glue("1. {name} ({year}): [publication](https://doi.org/{doi})", .envir = x)
  
  if(is.character(unlist(x[["trainingrepository"]])))
    res <- glue(res, "[training repository]({trainingrepository})", .sep = "; ", .envir = x)
  
  if(is.character(unlist(x[["modelrepository"]])))
    res <- glue(res, "[model repository]({modelrepository})", .sep = "; ", .envir = x)
  
  if(is.character(unlist(x[["webserver"]])))
    res <- glue(res, "[web server]({webserver})", .sep = "; ", .envir = x)
  
  paste0(res, ".")
}


dat <- readRDS("./data/full_dat.RDS")


cat_df <- data.frame(categories = c("antiangiogenic", "antibacterial", "antibiofilm", "anticancer", "antifungal", "antihypertensive", 
                                    "antiinflammatory", "antimicrobial", "antiparasitic", "antiviral", 
                                    "blood-brain barrier", "cell-penetrating", "chemotactic", "neuropeptides", 
                                    "quorum sensing", "surface-binding", "toxic"),
                     nice_names = c("Antiangiogenic peptides", "Antibacterial peptides (ABP)", 
                                    "Antibiofilm peptides", "Anticancer peptides (ACP)", "Antifungal peptides (AFP)", 
                                    "Antihypertensive peptides", "Antiinflammatory peptides (AIP)", 
                                    "Antimicrobial peptides (AMP)", "Antiparasitic peptides (APP)", 
                                    "Antiviral peptides (AVP)", "Blood-brain barrier peptides (BBP)", 
                                    "Cell-penetrating peptides (CPP)", "Chemotactic peptides ", 
                                    "Neuropeptides peptides", "Quorum sensing peptides", 
                                    "Surface-binding peptides", "Toxic peptides (TP)"))

all_tools <- sapply(1L:nrow(cat_df), function(ith_category_id) {
  
  part_dat <- dat[dat[[cat_df[ith_category_id, "categories"]]], ]
  row_order <- order(part_dat[["name"]])
  
  paste0(paste0("## ", cat_df[ith_category_id, "nice_names"], "\n\n"),
         paste0(sapply(row_order, function(ith_row)
           write_entry(part_dat[ith_row, ])
         ), collapse = "\n"), "\n\n\n")
})

links <- gsub(pattern = " ", replacement = "-", x = cat_df[["nice_names"]]) %>% 
  gsub(pattern = "[()]", replacement = "", x = .) %>% 
  tolower() %>% 
  paste0("(#", ., ")")

cat(paste0("- [", cat_df[["nice_names"]], "]", links), "\n\n", sep = "\n")

cat(all_tools, sep = "\n")
```

### About and citation

```{r, echo=FALSE,results='asis'}
cat(readLines("./data/citation.md"), sep = "\n")
```