Evidence map›Paper›PMID 39657630›Full record

ArticleBriefings in bioinformatics2024

Benchmarking recent computational tools for DNA-binding protein identification.

Xizi Luo, Amadeus Song Yi Chi, Andre Huikai Lin, Tze Jet Ong, Limsoon Wong, Chowdhury Rafeed Rahman

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 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. 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

6 authors.

Xizi LuoSchool of Computing, National University of Singapore, Singapore 119077, Singapore.
Amadeus Song Yi ChiSchool of Computing, National University of Singapore, Singapore 119077, Singapore.
Andre Huikai LinSchool of Computing, National University of Singapore, Singapore 119077, Singapore.
Tze Jet OngSchool of Computing, National University of Singapore, Singapore 119077, Singapore.
Limsoon WongSchool of Computing, National University of Singapore, Singapore 119077, Singapore.
Chowdhury Rafeed RahmanSchool of Computing, National University of Singapore, Singapore 119077, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of DNA-binding proteins (DBPs) is a crucial task in genome annotation, as it aids in understanding gene regulation, DNA replication, transcriptional control, and various cellular processes. In this paper, we conduct an unbiased benchmarking of 11 state-of-the-art computational tools as well as traditional tools such as ScanProsite, BLAST, and HMMER for identifying DBPs. We highlight the data leakage issue in conventional datasets leading to inflated performance. We introduce new evaluation datasets to support further development. Through a comprehensive evaluation pipeline, we identify potential limitations in models, feature extraction techniques, and training methods, and recommend solutions regarding these issues. We show that combining the predictions of the two best computational tools with BLAST-based prediction significantly enhances DBP identification capability. We provide this consensus method as user-friendly software. The datasets and software are available at https://github.com/Rafeed-bot/DNA_BP_Benchmarking.

Indexed as

BenchmarkingComputational BiologyDNA-Binding ProteinsSoftwareAlgorithmsHumansDNA-Binding ProteinsBLASTCD-HITdeep learningDNA-binding proteinmachine learningmotif

Identifiers

PMID39657630
PMCPMC11630855

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.