Introduction
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What is GenerativeProteomics?
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GenerativeProteomics is a framework designed for missing data imputation and augmentation in the field of proteomics, by 
using advanced generative models, like GAIN.


Why was it created ?
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Data missingness is a critical issue in many fields, particularly in proteomics, where large-scale datasets often have high rates of missing values, 
limiting the accuracy of downstream analysis and compromising breakthroughs. Their existence in datasets can undermine the integrity of the data, leading 
to biased analysis, loss of statistical power, model instability and wrongful conclusions. 
Traditional methods like dropping data or using simple imputations fail to handle the complexity and bias caused by missingness.
Advanced models have also been developed, but they each have limitations and cannot address all scenarios effectively.


All of these factors highlight the need to develop models and methods that can address this problem in an efficient and accurate way.
It was with the goal of addressing the recurrent and complex problem of missing data in proteomics that GenerativeProteomics was created.