Table 10.

Artificial intelligence-based RIP and RSP tools

Strategy	AI-based RIP and RSP tool	Description	Input	Output	Applicable Species	Active (T)/Inactive (F)
N-gram statistics language model	RIscoper (RNA Interactome Scoper) (RNA–RNA) [238]	The first tool for full-scale RNA interactome scanning via extraction of RRIs from the literature based on the N-gram model	Full texts or abstracts, with an online search tool connected to PubMed	Structured data of the extracted interactions in a machine-readable format such as interacting RNA partners, interaction types, contextual information, and metadata	All species	T
Score scheme (free energy parameter-refining approach based on ML)	Constraint generation- RNAsoft [241]	The first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data	RNA sequence, all on one line; and RNA secondary structure in dot-parentheses format, all on one line	Computation of the energy values as the solution to a constrained optimization problem, followed by an update on the optimisation function to better optimise the energy parameters	All species	T
Score scheme (weighed approach based on ML)	ContextFold [243]	An RNA secondary structure prediction tool that applies feature-rich scoring models, whose parameters are obtained after training on comprehensive datasets	One or more RNA sequences in FASTA format and accept MSA as input with optional structure constraints	Prediction of RNA secondary structures, including base pairs, loops, and stems Assignment of confidence score alongside prediction for quality assessment Energy parameters associated with the predicted structure Visual representations of the predicted structure	All species	T
Score scheme (a probabilistic approach based on ML)	Stochastic context-free grammars [168, 281, 282]	An alternative probabilistic methodology for modelling RNA secondary structure prediction based on the success of Hidden Markov Models in protein and gene modelling	An alignment of RNA sequences	Prediction of RNA secondary structure	N/A	N/A
Predicting process based on ML (end-to-end approach)	SPOT-RNA [246]	An RNA secondary structure prediction web tool using an ensemble of 2D deep neural networks and transfer learning	Single RNA sequence or batch of sequences	Prediction of RNA secondary structure Calculation of base-pair probability of predicted secondary structure, which is useful for plotting PR-curve and checking the confidence of predicted base-pair Generation of 2D plots via the VARNA tool [283]	Human	T
Predicting process based on ML (hybrid)	Deep learning method for state inference [284]	An improved RNA secondary structure prediction using state inference with deep recurrent neural networks	Dataset of known input–output pairs	Prediction of states for RNA secondary structure via a deep bidirectional LSTM model Generation of synthetic SHAPE data Prediction of RSP using the NNTM model, incorporating the predicted states and synthetic SHAPE data	Bacteria, animal, eukaryote, archaea	T
Predicting process based on ML (hybrid)	DMfold [247]	A method to predict RNA secondary structure with pseudoknots based on deep learning and improved base pair maximization principle	Target RNA sequences with dot-bracket sequences as labels	Prediction of RNA secondary structure with pseudoknots	All species	T
Predicting process based on ML (hybrid)	MINT [248]	An automatic tool for analysing 3D structures of RNA and DNA molecules, their full-atom molecular dynamics trajectories or other conformation sets	A simple text file with a detailed description of the RNA or DNA structure in each conformation frame	Determination of the hydrogen bonding network resolving the base pairing patterns for each RNA conformation Identification of secondary structure motifs (helices, junctions, loops, etc.), pseudoknots and short-range interactions in trajectories of NA Analysis of RNA/DNA 3D structure and their full-atom molecular dynamics trajectories or other conformation sets (e.g., X-ray or NMR-derived structures) Estimation of the energy of stacking and phosphate anion-base interactions, including the energetic features and their evolution.	All species	T
Predicting process based on ML (hybrid)	CONTRAfold (CONditional TRAining for RNA Secondary Structure Prediction) (RNA–RNA) [242]	A secondary structure prediction method based on conditional log-linear models, a flexible class of probabilistic models which generalise upon SCFGs by using discriminative training and feature-rich scoring	Single RNA sequence	Prediction of the best RNA structure Calculation of base-pair probability of predicted secondary structure	All species	T

2D: two-dimensional; 3D: 3-dimensional; AI: artificial intelligence; DNA: deoxyribonucleic acid; LSTM: long short-term memory; ML: machine learning; NA: nucleic acid; NMR: nuclear magnetic resonance; NNTM: nearest neighbor thermodynamic model; RIP: RNA–RNA interaction prediction; RNA: ribonucleic acid; RRI: RNA–RNA interaction; SCFG: stochastic context-free grammar; SHAPE: selective 2′-hydroxyl acylation analysed by primer extension