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Table 1 Software tools for predicting the structure and function of repeat sequences

From: Systematic identification and characterization of repeat sequences in African swine fever virus genomes

Tool name

Function

iEnhancer-EL [31]

Identifying enhancers with the ensemble learning approach

iPromoter-2L [32]

A two-layer predictor for identifying promoters by multiwindow-based K-tuple nucleotide composition

Espritz [33]

Detecting disordered regions from primary sequences by extracting the relevant information from the local context of the residue under consideration using the bidirectional recursive neural network

NetSurfP-2.0 [34]

Predicting the secondary structure for each residue of the input sequences by using an architecture composed of convolutional and long short-term memory neural networks

CAMPR3 [35]

Multiple machine learning algorithms for predicting antimicrobial peptides based on the amino acid sequence

MLCPP [36]

Machine-learning-based prediction of cell-penetrating peptides