ReviewEnvironment & health (Washington, D.C.)2025
Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.
Review in Environment & health (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continuous emergence of new pollutants poses significant threats to both human health and ecological environments. Nontarget analysis based on mass spectrometry has become prevalent for detecting new pollutants due to its high throughput capabilities. However, structural elucidation remains a major challenge in nontarget analysis. Here, we review the implementation of machine learning techniques to accelerate nontarget structural elucidation, with particular focus on spectral library matching, structural database retrieval, and de novo structure generation. We investigated the design principles, technical characteristics, and comparative evaluation of these computational approaches. In addition, we show their applications in environmental nontarget analysis for new pollutant identification. Finally, we discuss the challenges of current approaches and the future development trends. This review aims to deepen the understanding of existing computational approaches, promote the application of machine learning techniques in nontarget identification, and facilitate the integration of artificial intelligence with environmental pollutant analysis.
Indexed as
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.