ReviewMethods in molecular biology (Clifton, N.J.)2026
Machine Learning Approaches for Predicting RNA-RNA/DNA Interactions.
Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this chapter, I introduce machine learning approaches for predicting RNA-RNA/DNA interactions, which are crucial for understanding noncoding RNA (ncRNA) functions. Advancements in deep learning techniques and the availability of large-scale interaction data from high-throughput sequencing methods have driven the development of these prediction tools. This review covers representative studies across different RNA families, including prokaryotic small RNAs (TargetRNA3), general RNA-RNA interactions (CheRRI), miRNAs (DeepMirTar), box C/D snoRNAs (snoGloBe), lncRNA-DNA triplexes (triplexFPP), and CRISPR guide RNA design (CRISOT). These machine learning-based methods often improve accuracy compared to traditional energy-based approaches. However, there are challenges such as the need for preventing overfitting and third-party validation. Future advancements are expected to enhance the generalization and applicability of these prediction tools, contributing to a deeper understanding of RNA functions.
Indexed as
Identifiers
41174207What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.