iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC. (English) Zbl 1406.92189
Summary: Investigation into the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. Accordingly, it is crucially important to develop an automated method or high-throughput tool that can efficiently predict the PPIs. In this study, a new predictor, called “iPPI-PseAAC(CGR)”, was developed by incorporating the information of “chaos game representation” into the PseAAC (pseudo amino acid composition). The advantage by doing so is that some key sequence-order or sequence-pattern information can be more effectively incorporated during the treatment of the protein pair samples. The operation engine used in this predictor is the random forests algorithm. It has been observed via the cross-validations on the widely used benchmark datasets that the success rates achieved by the proposed predictor are remarkably higher than those by its existing counterparts. For the convenience of the most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iPPI-PseAAC(CGR), by which users can easily get their desired results without the need to go through the detailed mathematics.
MSC:
| 92C40 | Biochemistry, molecular biology |
| 92D20 | Protein sequences, DNA sequences |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 92-08 | Computational methods for problems pertaining to biology |
Keywords:
chaos game representation; pseudo amino acid composition; random forests; cross validation; Saccharomyces cerevisiae; Helicobacter pylori; web-serverSoftware:
pLoc-mAnimal; pLoc-mHum; iRNA-AI; pLoc-mEuk; iPPI-Esml; pLoc_bal-mGneg; iRNA-3typeA; iKcr-PseEns; iHSP-PseRAAAC; SMOTE; pLoc-mPlant; pLoc-mGneg; iNuc-PhysChem; iNuc-PseKNC; OOgenesis_Pred; iHyd-PseCp; iDNA6mA-PseKNC; iPPI-PseAAC; iProt-Sub; PREvaIL; iACP; FoldRate; iSNO-AAPair; pSuc-Lys; 2L-piRNA; iDNA-Prot; iROS-gPseKNC; iRO-3wPseKNC; pLoc_bal-mGpos; pLoc_bal-mHum; Quokka; iEnhancer-EL; iNitro-Tyr; iRSpot-EL; iRSpot-Pse6NC; iTIS-PseTNC; DPP-PseAAC; PredLactamase; BlaPred; RSARF; AFP-Pred; iSNO-PseAAC; iMethyl-PseAAC; iFeature; iCTX-Type; POSSUM; iRNAm5C-PseDNC; iRNA-2methyl; iPreny-PseAAC; iRNA-Methyl; iSS-PseDNC; iATC-mHyb; iRSpot-PseDNC; iRNA-PseColl; PseKNC; pLoc-mVirus; iSuc-PseOpt; iHyd-PseAAC; iPTM-mLys; pSumo-CD; propy; 2D-MH; iEnhancer-2L; Pse-in-One; Prnam-PC; iCar-PseCp; iRSpot-TNCPseAAC; iRNA-PseU; iLoc-lncRNA; iPPBS-Opt; iDrug-Target; iMiRNA-PseDPC; iOri-Human; iPro54-PseKNC; iPhos-PseEn; iPhos-PseEvo; PROSPERous; iDNA-Methyl; iPromoter-2L; LogitBoostReferences:
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