Skip to content

Effectiveness of AMP prediction using machine learning versus sequence similarity

Notifications You must be signed in to change notification settings

Legana/ML_vs_homology

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Machine learning AMP prediction models vs. homology (BLAST) to find AMPs in proteomes

The files required to run the code in these Rmd files can be obtained by clicking here or by using the command:

wget 'https://cloudstor.aarnet.edu.au/plus/s/yXYa5zVk5rrvRpz/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] ggtree_3.0.4        randomcoloR_1.1.0.1 broom_0.7.11        ggtext_0.1.1        pals_1.7           
 [6] treeio_1.16.2       ape_5.6-1           precrec_0.12.7      patchwork_1.1.1     ampir_1.1.0        
[11] forcats_0.5.1       stringr_1.4.0       dplyr_1.0.7         purrr_0.3.4         readr_2.1.1        
[16] tidyr_1.1.4         tibble_3.1.6        ggplot2_3.3.5       tidyverse_1.3.1    

loaded via a namespace (and not attached):
 [1] Rtsne_0.15           colorspace_2.0-2     ellipsis_0.3.2       class_7.3-19         Peptides_2.4.4      
 [6] fs_1.5.2             aplot_0.1.2          gridtext_0.1.4       dichromat_2.0-0      rstudioapi_0.13     
[11] listenv_0.8.0        prodlim_2019.11.13   fansi_1.0.2          lubridate_1.8.0      xml2_1.3.3          
[16] codetools_0.2-18     splines_4.1.2        knitr_1.37           jsonlite_1.7.2       pROC_1.18.0         
[21] caret_6.0-90         cluster_2.1.2        dbplyr_2.1.1         mapproj_1.2.8        compiler_4.1.2      
[26] httr_1.4.2           backports_1.4.1      assertthat_0.2.1     Matrix_1.3-4         fastmap_1.1.0       
[31] lazyeval_0.2.2       cli_3.1.0            htmltools_0.5.2      tools_4.1.2          gtable_0.3.0        
[36] glue_1.6.0           reshape2_1.4.4       maps_3.4.0           V8_4.0.0             Rcpp_1.0.8          
[41] cellranger_1.1.0     vctrs_0.3.8          nlme_3.1-153         iterators_1.0.13     timeDate_3043.102   
[46] gower_0.2.2          xfun_0.30            globals_0.14.0       rvest_1.0.2          lifecycle_1.0.1     
[51] future_1.23.0        MASS_7.3-54          scales_1.1.1         ipred_0.9-12         hms_1.1.1           
[56] parallel_4.1.2       yaml_2.2.1           curl_4.3.2           ggfun_0.0.4          yulab.utils_0.0.4   
[61] rpart_4.1-15         stringi_1.7.6        foreach_1.5.1        tidytree_0.3.7       lava_1.6.10         
[66] rlang_0.4.12         pkgconfig_2.0.3      evaluate_0.14        lattice_0.20-45      recipes_0.1.17      
[71] tidyselect_1.1.1     parallelly_1.30.0    plyr_1.8.6           magrittr_2.0.1       R6_2.5.1            
[76] generics_0.1.1       DBI_1.1.2            pillar_1.6.4         haven_2.4.3          withr_2.4.3         
[81] survival_3.2-13      nnet_7.3-16          future.apply_1.8.1   modelr_0.1.8         crayon_1.4.2        
[86] utf8_1.2.2           tzdb_0.2.0           rmarkdown_2.13       grid_4.1.2           readxl_1.3.1        
[91] data.table_1.14.2    ModelMetrics_1.2.2.2 reprex_2.0.1         digest_0.6.29        gridGraphics_0.5-1  
[96] stats4_4.1.2         munsell_0.5.0        ggplotify_0.1.0  

About

Effectiveness of AMP prediction using machine learning versus sequence similarity

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published