ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks.

Peter K Koo, Matt Ploenzke, Praveen Anand, Steffan Paul, Antonio Majdandzic

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Emerging RNA-centric technologies to probe RNA-protein interactions: importance in decoding the life cycle of positive sense single strand RNA viruses and antiviral discovery.Frontiers in cellular and infection microbiology · 2025
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Peter K KooSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. koo@cshl.edu.

Matt PloenzkeDepartment of Biostatistics, Harvard University, Cambridge, MA, USA.

Praveen AnandDana Farber Cancer Institute, Boston, MA, USA.

Steffan PaulBioinformatics Program, Harvard Medical School, Boston, MA, USA.

Antonio MajdandzicSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Funding

Single-Cell Biology Shared ResourceP30CA045508 · NCI · COLD SPRING HARBOR LABORATORY · PI John Edward Moses · 1987 to 2026

$118.9M

NCI NIH HHS P30 CA045508

6 · The paper itself

Abstract

Deep neural networks have demonstrated improved performance at predicting sequence specificities of DNA- and RNA-binding proteins. However, it remains unclear why they perform better than previous methods that rely on k-mers and position weight matrices. Here, we highlight a recent deep learning-based software package, called ResidualBind, that analyzes RNA-protein interactions using only RNA sequence as an input feature and performs global importance analysis for model interpretability. We discuss practical considerations for model interpretability to uncover learned sequence motifs and their secondary structure preferences.

Indexed as

Neural Networks, ComputerRNADNAPosition-Specific Scoring MatricesProtein BindingRNA-Binding ProteinsDNARNARNA-Binding ProteinsDeep learningGlobal importance analysisModel interpretabilityMotif analysisRNA-binding proteinsRNAcompeteRNA-protein interactions

Identifiers

PMID36705906

DOI10.1007/978-1-0716-2768-6_12

PMCPMC10245223

What OpenQuestion holds

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Registered trials

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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.