ArticlePloS one2016
kmlShape: An Efficient Method to Cluster Longitudinal Data (Time-Series) According to Their Shapes.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Proinflammatory state and metabolic dysregulation linking delayed feeding progression to extrauterine restricted head growth in extremely preterm infants.BMC medicine · 2025Article
- Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.BMC bioinformatics · 2025Article
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- The HuMet Repository: Watching human metabolism at work.Cell reports · 2024Article
- The developmental phenotype of motor delay in extremely preterm infants following early-life respiratory adversity is influenced by brain dysmaturation in the parietal lobe.Journal of neurodevelopmental disorders · 2024Article
- Predicting responses to omalizumab in antihistamine-refractory chronic urticaria: A real-world longitudinal study.The journal of allergy and clinical immunology. Global · 2024Article
- Trajectory clustering of immune cells and its association with clinical outcomes after aneurysmal subarachnoid hemorrhage.Frontiers in neurology · 2024Article
- Longitudinal Trajectory Modeling to Assess Adherence to Sacubitril/Valsartan among Patients with Heart Failure.Pharmaceutics · 2023Article
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- Tracking longitudinal biomarkers in burn patients with sepsis and acute kidney injury: an unsupervised clustering approach.European journal of medical research · 2023Article
- Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis.Scientific reports · 2023Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
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- Longitudinal profile of routine biomarkers for mortality prediction using unsupervised clustering algorithm in severely burned patients: a retrospective cohort study with prospectively collected data.Annals of surgical treatment and research · 2023Article
- A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age.Scientific reports · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
backgroundLongitudinal data are data in which each variable is measured repeatedly over time. One possibility for the analysis of such data is to cluster them. The majority of clustering methods group together individual that have close trajectories at given time points. These methods group trajectories that are locally close but not necessarily those that have similar shapes. However, in several circumstances, the progress of a phenomenon may be more important than the moment at which it occurs. One would thus like to achieve a partitioning where each group gathers individuals whose trajectories have similar shapes whatever the time lag between them.
methodIn this article, we present a longitudinal data partitioning algorithm based on the shapes of the trajectories rather than on classical distances. Because this algorithm is time consuming, we propose as well two data simplification procedures that make it applicable to high dimensional datasets.
resultsIn an application to Alzheimer disease, this algorithm revealed a "rapid decline" patient group that was not found by the classical methods. In another application to the feminine menstrual cycle, the algorithm showed, contrarily to the current literature, that the luteinizing hormone presents two peaks in an important proportion of women (22%).
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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.