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Several other methods, such as autocovariance (AC) [ 7] and amino acid index distribution [ 8] were developed to extract features such as physical chemical properties, frequencies, and locations of amino acids to represent a protein sequence.

The learning rate and the momentum of the model were the same for both human and other species, while the neurons and layers were tuned and adjusted according to the training set of different species. We used the newest version of HPRD dataset (2010 HPRD dataset) as one of the external test sets for our model. After removal of pairs shared with the benchmark dataset, 155465 of ‘high quality’ PPIs dataset and 459231 of ‘low quality’ PPIs dataset were obtained.It is worth noting that a one-hidden-layer with a medium neuron numbers was sufficient to train the dataset with relatively high accuracy; more layers and neuron numbers did not improve the predictive power. A more detailed summary of the application of the deep learning algorithm in computational biology can be found in a recent review [ 32].

The pre-training dataset was trained with 10-CV, and models with the best performance were selected to predict the hold-out test set.

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