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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Benchmarking antimicrobial peptide (AMP) machine learning models in a genome-scanning context
This repository looks at how best to predict AMPs in genomes and how to assess prediction performance.
- [Comparison of training and test data used by AMP predictors](01_data_comparison.md) : Rmd file [01_data_comparison.Rmd](01_data_comparison.Rmd)
- [Performance metrics in an omics-scanning context](02_performance_metrics.md) : Rmd file [02_performance_metrics.Rmd](02_performance_metrics.Rmd)
- [Benchmarking AMP predictors](03_benchmarking.md) : Rmd file [03_benchmarking.Rmd](03_benchmarking.Rmd)
The files required to run the code in these Rmd files can be obtained [here](https://cloudstor.aarnet.edu.au/plus/s/Hd51gUnXdCq0nEg) or by using the command:
```bash
wget 'https://cloudstor.aarnet.edu.au/plus/s/Hd51gUnXdCq0nEg/download' -O data.tgz
tar -zxvf data.tgz
```
### `sessionInfo()`
```
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.3.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] caret_6.0-90 lattice_0.20-45 stringdist_0.9.8 ComplexUpset_1.3.3 patchwork_1.1.1
[6] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[11] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 ampir_1.1.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.2 splines_4.1.2 foreach_1.5.1 prodlim_2019.11.13
[6] modelr_0.1.8 assertthat_0.2.1 stats4_4.1.2 cellranger_1.1.0 yaml_2.2.1
[11] globals_0.14.0 ipred_0.9-12 pillar_1.6.4 backports_1.4.1 glue_1.6.0
[16] pROC_1.18.0 digest_0.6.29 rvest_1.0.2 colorspace_2.0-2 recipes_0.1.17
[21] htmltools_0.5.2 Matrix_1.3-4 plyr_1.8.6 timeDate_3043.102 pkgconfig_2.0.3
[26] broom_0.7.11 listenv_0.8.0 haven_2.4.3 scales_1.1.1 gower_0.2.2
[31] lava_1.6.10 tzdb_0.2.0 generics_0.1.1 ellipsis_0.3.2 withr_2.4.3
[36] nnet_7.3-16 cli_3.1.0 survival_3.2-13 magrittr_2.0.1 crayon_1.4.2
[41] readxl_1.3.1 evaluate_0.14 fs_1.5.2 future_1.23.0 fansi_1.0.2
[46] parallelly_1.30.0 nlme_3.1-153 MASS_7.3-54 xml2_1.3.3 class_7.3-19
[51] tools_4.1.2 data.table_1.14.2 hms_1.1.1 lifecycle_1.0.1 reprex_2.0.1
[56] munsell_0.5.0 compiler_4.1.2 rlang_0.4.12 grid_4.1.2 rstudioapi_0.13
[61] iterators_1.0.13 rmarkdown_2.13 gtable_0.3.0 ModelMetrics_1.2.2.2 codetools_0.2-18
[66] DBI_1.1.2 reshape2_1.4.4 R6_2.5.1 lubridate_1.8.0 knitr_1.37
[71] fastmap_1.1.0 future.apply_1.8.1 utf8_1.2.2 stringi_1.7.6 parallel_4.1.2
[76] Peptides_2.4.4 Rcpp_1.0.8 vctrs_0.3.8 rpart_4.1-15 dbplyr_2.1.1
[81] tidyselect_1.1.1 xfun_0.30
```