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.
Finally, @cite_10 present a fully connected model regulated by dropouts. Like @cite_0 , they use composition-transition-distribution descriptors as features. They apply a 5-fold cross validation and have no separated test sets.
@cite_0 @cite_10
2616246685 2804331675
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein–protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein–Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50 , Precision of 94.38 , Recall of 90.56 , Specificity of 94.49 , Matthews Correlation Coefficient of 85.08 and Area Under the Curve of 97.43 , respectively. Extensive experiments show that DeepPPI can learn useful feat... Abstract Protein–protein interactions (PPIs) are of vital importance to most biological processes. Plenty of PPIs have been identified by wet-lab experiments in the past decades, but there are still abundant uncovered PPIs. Furthermore, wet-lab experiments are expensive and limited by the adopted experimental protocols. Although various computational models have been proposed to automatically predict PPIs and provided reliable interactions for experimental verification, the problem is still far from being solved. Novel and competent models are still anticipated. In this study, a neural network based approach called EnsDNN (Ensemble Deep Neural Networks) is proposed to predict PPIs based on different representations of amino acid sequences. Particularly, EnsDNN separately uses auto covariance descriptor, local descriptor, and multi-scale continuous and discontinuous local descriptor, to represent and explore the pattern of interactions between sequentially distant and spatially close amino acid residues. It then trains deep neural networks (DNNs) with different configurations based on each descriptor. Next, EnsDNN integrates these DNNs into an ensemble predictor to leverage complimentary information of these descriptors and of DNNs, and to predict potential PPIs. EnsDNN achieves superior performance with accuracy of 95.29 , sensitivity of 95.12 , and precision of 95.45 on predicting PPIs of Saccharomyces cerevisiae. Results on other five independent PPI datasets also demonstrate that EnsDNN gets better prediction performance than other related comparing methods.