Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies.

Hasan Zulfiqar, Ramala Masood Ahmad, Ali Raza, Sana Shahzad, Hao Lin

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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.

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3 · Its place in the literature

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1 citing paper in PubMed.

ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026
Review

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hasan ZulfiqarYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China. hasanzulfiqar@uestc.edu.cn.

Ramala Masood AhmadDepartment of Plant Breeding and Genetics, University of Agriculture Faisalabad, Faisalabad, Pakistan.

Ali RazaYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.

Sana ShahzadInstitute of Horticultural Sciences, University of Agriculture Faisalabad, Faisalabad, Pakistan.

Hao LinYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China. hlin@uestc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Promoters are the genomic regions upstream of genes that RNA polymerase binds in order to initiate gene transcription. Understanding the regulation of gene expression depends on being able to identify promoters, because they are the most important component of gene expression. Agrobacterium tumefaciens (A. tumefaciens) strain C58 was the subject of this study with the goal of creating a machine learning-based model to predict promoters. In this study, nucleotide density (ND), k-mer, and one-hot were used to encode the promoter sequence. Support vector machine (SVM) on fivefold cross-validation with incremental feature selection (IFS) was used to optimize the generated features. These improved characteristics were then used to distinguish promoter sequences by feeding them into the random forest (RF) classifier. Tenfold cross-validation (CV) analysis revealed that the projected model has the ability to produce an accuracy of 84.22%.

Indexed as

Agrobacterium tumefaciensArtificial IntelligencePromoter Regions, GeneticSupport Vector MachineAlgorithmsComputational BiologyAgrobacterium tumefaciensFeature engineeringMachine learningPromotersRandom forest

Identifiers

PMID39068330

DOI10.1016/j.compbiomed.2023.107165

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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.