Evidence map›Paper›PMID 40594227›Full record

ArticleScientific reports2025

Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application.

Anupam Yadav, B Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M Janaki Ramudu, B Bharathi, Prabhat Kumar Sahu

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

9 authors.

Anupam YadavDepartment of Computer Engineering and Application, GLA University, Mathura, 281406, India. anupam.yadav@gla.ac.in.
B JayaprakashDepartment of Computer Science & IT, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Laith Hussein JasimDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq.
Mayank KundlasCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
Maan Younis AnadDepartment of Cybersecurity, College of Engineering Technology, Alnoor University, Mosul, Iraq.
Ankur SrivastavaDepartment of CSE, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, India.
M Janaki RamuduDepartment of CSE, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, 531162, India.
B BharathiDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
Prabhat Kumar SahuDepartment of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. The release of medication was measured using Raman spectroscopy and the data was used for estimation of drug delivery using machine learning models. Raman data was used along with some inputs including coating type, medium, and release time to estimate the drug release as the sole target. The study explores predictive modeling for the release variable in a dataset including 155 samples with over 1500 spectral variables. Partial least squares (PLS) was applied for dimensionality reduction, and models such as AdaBoost with linear regression, multilayer perceptron (MLP), and Theil-Sen regression were utilized, achieving the highest predictive performance with the AdaBoost-MLP model (R

Indexed as

Drug Delivery SystemsDrug LiberationMachine LearningLeast-Squares AnalysisSpectrum Analysis, RamanDrug deliveryLinear regressionMachine learningMultilayer perceptronTheil-Sen regression

Identifiers

PMID40594227
PMCPMC12216472

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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.