DOI: 10.5281/zenodo.16642475
title: CLASPP Training Testing Validation data (both initial prototype data and finalized data)

description:  

There are 2 versions of data sets included in this repo: the prototype (24_05_07) and finalized (25_05_11) 

The major difference between Prototype and the finalized data sets are as following  



Prototype has more unsupervised custering labels (60) then finalized (54) 

Prototype has more clusters associated with Y-Phos, K-Sumo K-Malo      

Prototype has K-Succ and finalized does not  

Finalized has PK-Hydr and Prototype does not

They are put through the same curation pipeline but with different random seeds for sampling  


Most difference are an outcome of testing the stability of each PTM type (class) in a multi-classification setting. Most PTM (All that was tested) are stable (will converge) in a single binary classification setting. K-Succ when added to the multi-classification tanked its performance and other PTM types (anecdotal behavoir testing). The swap to a different finalized training set was primarily due to this clash in performance. In theory with different hyper-parameters, one could fix this. Difference 3 being the major reason and all other differences are just to tell a better story and cleaning up the data to coincide with benchmarks performance from Fig2 and S_Fig2. 

    

      

 Finalized (25_05_11) 



train_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to60NegRaio-25_05_11.csv 



Used to train the final model and this was utilized in Fig4, Fig5, Fig6, S_Fig3, and S_fig4 



val_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to1NegRaio-25_05_11.csv  



Used to help train the final model  



test_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to1NegRaio-25_05_11.csv  



Used as the benchmark in Fig4 and S_Fig3 

Fig 4 and S_Fig3 used the positive labels and just neg labels that share the same res type as the negative class in this benchmark 

Same positive labels as HUMAN_labs.txt




All data in the finalized data set are labeled using the unsupervised clustering labels (54) rather than final labels (20)  

 

      

Prototype (24_05_07)   



train_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to60NegRaio-24_05_07.csv  



Used to train initial model that was used to in Fig2, Fig3, S_Fig1, S_Fig2 

segmented and made a Singel Binary Classification models for Fig2 and S_Fig2 



val_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to1NegRaio-24_05_07.csv  



Used to help train initial model and used to benchmark Fig2, Fig3, S_Fig1, S_Fig2 

Fig3 and S_Fig1 used the positive labels and all negative labels negative class in this benchmark 

Fig2 and S_Fig2 used the positive labels and all negative labels negative class in this benchmark (Singel Binary Classification) 




All data in the prototype data set are labeled using the unsupervised clustering labels (60) rather than final labels (20)  

 

 

 

Out-of-distribution  



    HUMAN_labs.txt 

    MOUSE_labs.txt 

    DROME_labs.txt 

    CAEEL_labs.txt 

    YEAST_labs.txt 

    ECOLI_labs.txt  


    All benchmarks here use final labels (20) rather than the unsupervised clustering labels (54)  

    Negative labels were only used if they share the residue to the positive label 

    All positive and negative labels were under-sampled to have a max of 500 

    Each species specific PTM type benchmark was only used if they have at least 100 positive examples and 50 negatives  

  

 

 

 Different Res and Neg ratios 

(not good performance in practice but could work in theory)  



    train_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to70NegRaio-24_05_07.csv  



    Pos/Neg ratio is 1/70 (each class) - Medium/Easy neg Res ration is uniform 1/20 - tot res ratio NOT uniform 



    train_hd3_CustBL62SeqDistSpecClus_uniResRatio_1to100NegRaio-24_05_07.csv  



    Pos/Neg ratio is 1/100 (each class) - Medium/Easy neg Res ration is uniform 1/20 - tot res ratio NOT uniform 



    train_hd3_CustBL62SeqDistSpecClus_uniResRatio_CustNegRaio-24_05_07.csv  



    Pos/Neg ratio is 1/1000 (each class) - Medium/Easy neg Res ration is NOT uniform - tot res ratio is uniform 




 

 

Other related repos




Repo
Link (will go live when submitted)
Discription


GitHub
github_version_Data_cur
This verstion contains code but but no data. It needs you to run the code to generate all the helper-files (will take some time run this code)


GitHub
github_version_Forward
This verstion contains code but NOT any weights (file too big for github)


Huggingface
huggingface_version_Forward
This verstion contains code and training weights


Zenodo
zenodo_version_training_data
zenodo version of training/testing/validation data


webtool
webtool
webtool hosted on a server