Table 2.

Currently available tools for PPI prediction.

Predictor	ML Algorithms	Encoding Methods	Testing Methods	Accuracy	Year	Predictor URL	References
Pred_PPI	SVM	Auto covariance	Jackknife	90.67% (human), 88.99% (yeast), 90.09% (Drosophila), 92.73% (E. coli), 97.51% (C. eleganse)	2010	http://cic.scu.edu.cn/bioinfor-matics/predict_ppi/default.html	[72]
Hotpoint	SVM	PseAAC and local alignment kernel	5-fold CV	70%	2010	http://prism.ccbb.ku.edu.tr/hotpoint/	[89]
PSOPIA	Domain-based	Sequence similarity	10-fold CV	70-85%	2014	http://mizuguchilab.org/PSOPIA	[80]
NIP	SVM	G-gap dipeptide compositions	Jackknife	92.67%	2016	http://mlda.swu.edu.cn/codes.php?name=NIP	[70]
SPRINT	SVM	k-mer	10-fold	N/A	2017	https://github.com/lucian-ilie/SPRINT/	[71]
SIPMA	RF	Autocorrelation, AAC, PseAAC	10-fold CV	89.9%	2018	http://kurata14.bio.kyutech.ac.jp/SIPMA/	[46]
DPPI	Deep learning	Sequence features	10-fold CV	96%	2018	https://github.com/hashemifar/DPPI/	[77]
PPI-Detect	SVM	BPF and sequence features	10-fold CV	91.40%	2018	https://ppi-detect.zmb.uni-due.de/	[47]
DLPred	Deep learning	PSSM, HI, AAindex, sequence conservation score, and 3D-1D scores.	10-fold CV	73.68%	2019	http://qianglab.scst.suda.edu.cn/dlp/	[75]
GWORVMBIG	Optimizer-Based Relevance Vector Machine	PSSM and evolutionary encoding	5-fold CV	NA	2019	http://219.219.62.123:8888/GWORVMBIG	[76]
DAMpred	Neural-Network	Protein structure encoding	10-fold	86%	2019	https://zhanglab.ccmb.med.umich.edu/DAMpred	[73]
FCTP-WSRC	SVM and Weighted sparse leraning	Auto covariance and KNN	5-fold CV	96.67%, 99.82%, and 98.09% for H. pylori, Human and Yeast	2020	https://github.com/wowkiekong/PPI-prediction	[74]