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We compared the sequence similarities of the positive and negative samples of the 2005 Martin dataset between the positive and negative samples of the benchmark dataset, respectively, and found the unsatisfied result might be due to that the negative samples of 2005 Martin dataset was much similar to the positive samples of the benchmark dataset rather than similar to the negative samples of benchmark dataset (Additional file 8: Table S7). We then tested the positive and negative samples separately and found the prediction accuracy for the positive samples was as high as 94. Because the hidden layer and the neuron numbers for each layer of SAE are both critical parameters, we tried different combinations; Details of these combinations are shown in Additional file 5 Figures S3, S4 and Table S4.

Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Proteins interact with one another through a group of amino acids or domains, so the success of our SAE algorithm may be due to its powerful generalization capacity on protein sequence input codons to learn hidden interaction features. Biochemical assays, chromatography, and similar small-scale experimental methods have long been used to identify novel PPIs, but these only contribute to a low coverage of the whole PPI database due to their poor efficacies [ 2]. Of these, some have attempted to mine new protein information, whereas others involved the development of new machine-learning algorithms. Deep-learning algorithms have been used in many fields and their applications in bioinformatics are increasing.So, we obtained robust performance on 10-CV training, and for predicting the hold-out and the NR-test sets. a) Protein interaction network of the positive samples from the benchmark dataset and (b) negative pairs’ network from the benchmark dataset.

Because the AC coding method was superior to the CT coding method for this task, we used AC in the subsequent model construction. SVHC declaration according to the regulation EC 1907/2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). When the Co-Browse window opens, give the session ID that is located in the toolbar to the representative. Sequences annotated with ambiguous or uncertain subcellular location terms, such as “potential”, “probable”, “probably”, “maybe”, or “by similarity”, were excluded. All PPI pairs in DIP dataset are listed in Additional File 7) was also tested, and this yielded a prediction accuracy of 93.The negative samples from each species were also created by pairing proteins from different subcellular locations, and, in all cases, the number of negative samples was equal to the number of positive samples.