  Biological data are extremely diverse, complex but also quite sparse. The
recent developments in deep learning methods are offering new possibilities for
the analysis of complex data. However, it is easy to be get a deep learning
model that seems to have good results but is in fact either overfitting the
training data or the validation data. In particular, the fact to overfit the
validation data, called "information leak", is almost never treated in papers
proposing deep learning models to predict protein-protein interactions (PPI).
In this work, we compare two carefully designed deep learning models and show
pitfalls to avoid while predicting PPIs through machine learning methods. Our
best model predicts accurately more than 78% of human PPI, in very strict
conditions both for training and testing. The methodology we propose here allow
us to have strong confidences about the ability of a model to scale up on
larger datasets. This would allow sharper models when larger datasets would be
available, rather than current models prone to information leaks. Our solid
methodological foundations shall be applicable to more organisms and whole
proteome networks predictions.
