ArticleACS omega2026
One-Class Genetic Algorithm for Authentication Analysis of Spectrochemical Data.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One-class classifier (OCC) models are widely applied to solve classification problems where control or class modeling from a target class is necessary. In this study, OCC models such as Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) were associated with a new variable selection strategy, the one-class genetic algorithm (OGA), for classification analyses in three clinical applications: COVID-19, endometriosis, and dengue samples. DD-SIMCA was implemented in a rigorous approach, using α = 0.05, while OCPLS was performed with partial robust M-regression (PRM). For the three cases, a better classification performance was obtained using the OGA associated with the OCC model. The performance of the OGA-PRM-OCPLS showed better results for both COVID-19 and endometriosis cases when compared to DD-SIMCA, with a classification sensitivity of 100%. However, the best results for dengue classification were obtained by using the OGA-DD-SIMCA model (sensitivity = 100%). The selected variables obtained by the OGA can be used to relate this information to biomarkers capable of distinguishing between case and control groups. These findings have the potential to improve some disease diagnosis using chemometrics for the development of rapid, low-cost, and minimally invasive screening methodologies.
Identifiers
What OpenQuestion holds
Registered trials
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.