ArticleScientific reports2021
Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
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Who cites it
7 citing papers in PubMed.
- Similarity measure of time series based on Angle-distance Penalized Metric Dynamic Time Warping.Scientific reports · 2025Article
- Exploiting Salmonella typhimurium as a novel cancer therapeutic agent: from tumor colonization to metabolic engineering.World journal of microbiology & biotechnology · 2025Review
- Protein Intake and Protein Quality Patterns in New Zealand Vegan Diets: An Observational Analysis Using Dynamic Time Warping.Nutrients · 2025Observational
- Somtimes: self organizing maps for time series clustering and its application to serious illness conversations.Data mining and knowledge discovery · 2024Article
- Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories.International journal of health geographics · 2023Article
- Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics.Journal of chemical theory and computation · 2023Article
- Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California.Applied sciences (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM
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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.