Table 3.

A comprehensive list of the reviewed methods/tools for prediction of prokaryotic and eukaryotic promoters^a

Method type	Toolb	Year	Webserverc	Features/Motifs	Scoring function /Algorithm	Evaluation strategy	Species and promoter type	Sequence length (bp)
Deep learning–based	CNNProm [30]	2017	Yes	–	CNN	70% train, 20% test, 10% validation	H. sapiens (TATA-containing and TATA-less), M. musculus (TATA-containing and TATA-less), A. thaliana (TATA-containing and TATA-less), E. coli () and B. subtilis	81, 251
Traditional machine learning–based	Rani et al.-I [132]	2007	No	DNC	ANN	5-fold CV and independent test	E. coli (), and D. melanogaster	80, 241
	Rani et al.-II [133]	2009	No	n-gram (n=2,3,4,5)	ANN	5-fold CV and independent test	E. coli () and D. melanogaster	80, 300
	iProEP [31]	2019	Yes	PseKNC and PCSF	SVM	5, 10-fold CV	D. melanogaster, H. sapiens, C. elegans, E. coli () and B. subtilis ()	81, 300
Scoring function–based	IPMD [134]	2010	No	PCSF and ID	Modified MD	10-fold CV and independent test	D. melanogaster, H. sapiens, C. elegans, E. coli () and B. subtilis ()	81, 300

^aAbbreviations: CNN—convolutional neural network; DNC—dinucleotide composition; ANN—artificial neural network; CV—cross-validation; PseKNC—pseudo–K-tuple nucleotide composition; PCSF—position-correlation scoring function; SVM—support vector machine; ID—increment of diversity; MD—Mahalanobis Discriminant.

^bThe URL addresses for the predictors with functioning webserver: CNNProm—http://www.softberry.com/berry.phtml?topic=cnnprom&group=programs&subgroup=deeplearn; iProEP—http://lin-group.cn/server/iProEP/pages/download.php.

^cYes—The approach is accompanied with a webserver/tool and it is still working; Decommissioned—The webserver/tool is no longer available; No—The approach has no webserver or tool.