Evidence map›Paper›PMID 39804669›Full record

ArticleBioinformatics (Oxford, England)2024

EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.

Yubo Wang, Haoran Zhu, Yansong Wang, Yuning Yang, Yujian Huang, Jian Zhang, Ka-Chun Wong, Xiangtao Li

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. 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.

  1. Article
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

8 authors.

Yubo WangSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Haoran ZhuSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.ORCID 0000-0002-7240-2696
Yansong WangSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Yuning YangInformation Science and Technology, Northeast Normal University, Changchun 130024, China.ORCID 0000-0003-4556-139X
Yujian HuangCollege of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China.
Jian ZhangSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.ORCID 0000-0001-7155-7760
Ka-Chun WongDepartment of Computer Science, City University of Hong Kong, Hong Kong SAR 999077, China.ORCID 0000-0001-6062-733X
Xiangtao LiSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.ORCID 0000-0002-8716-9823

Funding

China National Natural Science Foundation 62472195
6 · The paper itself

Abstract

motivationPredicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

resultsEnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RBPs. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA-binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RBP sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights. AVAILABILITY AND IMPLEMENTATION: EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.

Indexed as

Computational BiologyRNA-Binding ProteinsSoftwareAlgorithmsBinding SitesDeep LearningMachine LearningRNARNARNA-Binding Proteins

Identifiers

PMID39804669
PMCPMC11783304

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.