Evidence map›Paper›PMID 39415085›Full record

ArticleBMC plant biology2024

Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum.

Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC plant biology, 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

3 authors.

Maryam Mazaheri-TiraniDepartment of Biology, Faculty of Science, University of Jiroft, Jiroft, 78671-61167, Iran. mazaheri@ujiroft.ac.ir.ORCID https://orcid.org/0000-0003-4643-8967
Soleyman DayaniDepartment of Biotechnology, Faculty of Agriculture and Natural Resources, Imam Khomeini International University (IKIU), Qazvin, 34149-16818, Iran.ORCID https://orcid.org/0000-0002-8639-7867
Majid Iranpour MobarakehDepartment of Computer Engineering and IT, Payame Noor University, Tehran, Iran.ORCID https://orcid.org/0000-0003-3009-6093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H

Indexed as

Machine LearningZinc OxideAlgorithmsMetal NanoparticlesNanoparticlesZinc OxideArtificial intelligence (AI)Media pH alterationMetal toxicityOxidant and antioxidantsPlant pigmentsPrimary metabolitesZinc bioavailability

Identifiers

PMID39415085
PMCPMC11481599

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.