Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

Table 1

Overview of the methods concerning lncRNA identification.

	Published year	Testing datasets	Training species	Model	Query file format	Web interface
CPC	2007		Eukaryotic	SVM	FASTA	Yes
CPAT	2013		Human; mouse; fly; zebrafish	LR	BED; FASTA	Yes
CNCI	2013	lncRNA	Human; plant	SVM	FASTA; GTF	No
PLEK	2014	lncRNA	Human; maize	SVM	FASTA	No
lncRNA-MFDL	2015	lncRNA	Human	DL
LncRNA-ID	2015	lncRNA	Human; mouse	RF	BED; FSATA	No
lncRScan-SVM	2015	lncRNA	Human; mouse	SVM	GTF	No
LncRNApred	2016	lncRNA	Human	RF	FASTA	Web only

Testing datasets denote that one specific method is developed to discriminate ncRNAs or lncRNAs from protein-coding transcripts. The classification model of CPC, CNCI, PLEK, and lncRScan-SVM is support vector machine (SVM); CPAT employs logistic regression (LR); LncRNA-ID and LncRNApred utilise random forests (RF) and lncRNA-MFDL uses deep stacking networks (DSNs) of deep learning (DL) algorithm.
that the most popular tool CPC is trained and tested on datasets of ncRNAs and protein-coding transcripts. The training datasets of CPAT are also ncRNAs and protein-coding transcripts, though test on lncRNAs for CPAT is conducted and achieved a higher accuracy.
access link of lncRNA-MFDL has expired; thus, we cannot verify the information that the original paper failed to mention.