Recently, a large number of human PPIs have been verified due to the continually development of the high-throughput technologies. We selected two comprehensive databases that integrated most of the newly-updated PPIs databases (see the Database section) to test our model. The prediction accuracy of the HIPPIE HQ was 92.24% while the prediction accuracy of the HIPPIE LQ was 89.72%. The prediction accuracy of the inWeb_inbiomap HQ was 91.14% while the prediction accuracy of the inWeb_inbiomap LQ was 87.99%. We noticed that our model had better prediction on the HQ dataset than the LQ dataset. We also submitted the HIPPIE HQ dataset to Pan’s server, and the returned prediction accuracy was 85.01%, which was lower than that of our model (92.24%). Zeng HY, et al. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics. 2016;32(12):i121–7. HPRD NR dataset: we removed all pairs in the 2010 HPRD dataset with a pairwise identity ≥25% to those in the benchmark dataset, after which, a total of 1,482 pairs remained. Protein-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. Results Then AC and CT models with the best performance with 10-CV were recruited to predict the hold-out test set. The AC model achieved an accuracy of 96.82%, whereas the CT model 94.47%. We removed all pairs in the hold-out test set with ≥25% pairwise identity with those in training set (NR-test set) and used these to confirm the models. The predictive abilities of both models did not decrease appreciably with the NR-test set (Table 1). So, we obtained robust performance on 10-CV training, and for predicting the hold-out and the NR-test sets. Because the AC coding method was superior to the CT coding method for this task, we used AC in the subsequent model construction.

Hinton G, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82–97. Hassell-Smith, A. ‘Labourers in Late Sixteenth-Century England: A Case Study from North Norfolk [Part II], Continuity & Change 4:3 (1989), 367-94. Petition of Holcraft Blood, Esq., to the King, showing that he was appointed second engineer of England in Jan. 1695 upon the Office of Ordnance, at 250 l. per ann. and 13 s. 4 d. a day travelling charges, and that he was appointed by commission of 1 Oct. 1696 director and commander-in-chief of the- King's company of Engineers, and had had no pay for the same; seeking to be allowed 13 s. 4 d. a day from 1 Oct. 1696 to 25 March 1699, when the company “was broke.” The reply of Walter Devereux to the answer made by Mr. Henry Baker, to six articles of complaint exhibited to the Lords of the Treasury, against him (Mr. Baker) by the said Devereux, together with the proofs of the said charge. Letter from Mr. Wm. Blathwayt, probably to Mr. Lowndes, desiring that he would remind their Lordships of his just pretensions as to his salary for 1699, for executing the office of Secretary of State. Dated Whitehall, 23 Jan. 1700.Zhang YN, et al. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence. J Theor Biol. 2011;283(1):44–52. You ZH, et al. A MapReduce based parallel SVM for large-scale predicting protein-protein interactions. Neurocomputing. 2014;145:37–43. The petitioner was descended from the Duncans of Scotland by her father, and by her mother from “the Lord Viscount Loftis of Ely's family,” who was Lord Chancellor of Ireland, High Justice of the kingdom and one of the Council. Their dwelling in England was formerly Middleham Castle in Yorkshire, but the petitioner by these losses was exposed in the world to get her livelihood. The Lord “Lisbon,” who was killed in the King's service in Ireland (being the petitioner's relation), spoke to the Duke of Bolton to move the King for a yearly pension for her life on His Majesty's accession, but was advised to defer it until the wars were ended. The petitioner had a sister to be provided for out of what should be settled on her: praying the use of the 10,000 l. above mentioned for the lives of herself and sister, and for bounty to discharge several debts that lay hard on her, and for their support until a pension should be settled. If desired, coverslips can be sealed around the perimeter with nail polish or a plastic sealant. Mounted slides should be stored at 4 °C, protected from light.

Minuted:—“22 Jan. 1700, admitt the like composic[i]ons to be made for the 9 ships, or such of them as ye owners desire to compound for.”

You ZH, et al. Large-scale protein-protein interactions detection by integrating big biosensing data with computational model. Biomed Res Int. 2014;2014:598129. The opinion of the Attorney and Solicitor-General (Tho. Trevor and Jo. Hawles) for the guidance of the Lords of the Treasury, on a demand made by Mr. Williamson at the Tally Court, through Mr. Le Neve, one of the Deputy Chamberlains of the Exchequer, for an annuity of 60 l. and 30 l. in addition, viz., as to the taking the same out of the hereditary excise. Dated 14 Feb. 1700. Our final model was used to predict the external test sets. We used the newest version of HPRD dataset (2010 HPRD dataset) as one of the external test sets for our model. After excluding the protein pairs that are same in the benchmark dataset, a total of 9,214 PPI were obtained. Our model yielded a prediction accuracy of 99.21%. After the removal of the protein pairs with a ≥25% pairwise sequence identity to those in the benchmark dataset (the 2010 HPRD NR dataset), the prediction accuracy was still high (97.14%) (See more details about the redundancy removal in Additional File 6). We compared our results with Guo’s work. Using the 2009 version of HPRD to test their model, which was based on AC coding and SVM algorithm, Guo et al. achieved a prediction accuracy of 93.59% [ 35]. Redundancy removal of their test sets resulted in a prediction accuracy of 93.09%. This demonstrated a better prediction capacity of our model. The refractive index for VECTASHIELD Mounting Medium is 1.45. VECTASHIELD Mounting Medium Antifade Comparison