BackgroundProtein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested.ResultsWe used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods.ConclusionsTo our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1700-2) contains supplementary material, which is available to authorized users.
Standard methods for body fluid identification typically rely on detection of the functional proteins specific to or enriched in them, such as hemoglobin in blood, alkaline phosphatase and PSA in semen, or α-amylase in saliva. While these markers can be relatively specific, the multiple methods used to identify them frequently rely on nonspecific chemical, enzymatic, or antibody reactions that usually require the structural integrity of the markers and are not confirmatory because other proteins or substances can also give positive test results. Recent advances in proteomics and mass spectrometry offer the ability to simultaneously detect multiple body fluid protein markers in a single, confirmatory test. Here, multiple markers for blood, saliva, and semen are identified by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). Data demonstrate the ability to detect these body fluids at nanoliter to subnanoliter levels and to distinguish mixtures. Protein stability of mock samples assayed after 16 months showed no diminution of signal. Because multiple peptides from multiple protein markers are detected and effectively sequenced by MALDI MS/MS, the assay is confirmatory. As mass spectrometry detects whatever peptides are present in a sample, no a priori knowledge of an unknown stain is necessary to perform the test.
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