Skip to main content

Table 9 Description of the algorithms and features used for each method

From: GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning

Method

Feature

Oversampling

Algorithm

The number of subcellular compartments

DeepLncRNA [26]

k-mer, Genome loci, RNA binding motifs

–

Neural networks

2

lncLocator 2.0 [27]

–

–

CNN, LSTM, Multi-layer perceptron

2

iLoc-lncRNA [14]

PseKNC

–

SVM

4

Locate-R [28]

k-mer, n-gapped k-mer

SMOTE

Locally Deep SVM

4

LncLocPred [30]

k-mer, PseDNC, Triplet

–

Logistic regression

4

lncLocator [25]

k-mer

SOS [55]

Random forest, SVM, Neural networks

5

DeepLncLoc [31]

k-mer

–

TextCNN

5

GM-lncLoc (Ours)

k-mer

SMOTE

GCN based on MAML

4 or 5