ArticleGigaScience2020
Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Article
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- Resolving Heterogeneity in the Diagnosis of Alzheimer's Disease and its Progression Using Multimodal Data.Journal of molecular neuroscience : MN · 2026Article
- Clinical Phenotypes of Critically Ill Patients with COVID-19 Infected with Omicron: A Nationwide Prospective Cohort Study.Infectious diseases and therapy · 2026Article
- Predicting complications and mortality in myocardial infarction patients using a graph neural network model.Scientific reports · 2026Article
- Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing.Frontiers in aging neuroscience · 2026Article
- Reply to Takefuji: Limitations of Linear Dimensional Reduction Methods in Chronic Obstructive Pulmonary Disease Phenotyping.American journal of respiratory and critical care medicine · 2025Article
- Identification and Validation of New Molecular Subtypes within the Early and Late Mild Cognitive Impairment Stages of Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Navigating the Progression of Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2025Article
- Temporal Exploration of Chronic Obstructive Pulmonary Disease Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts.American journal of respiratory and critical care medicine · 2025Observational
- A Hands-On Introduction to Data Analytics for Biomedical Research.Function (Oxford, England) · 2025Review
- Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors.Nature communications · 2025Article
- A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Improving the performance and interpretability on medical datasets using graphical ensemble feature selection.Bioinformatics (Oxford, England) · 2024Article
- Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms.The journal of Tehran Heart Center · 2023Article
- Rosenblatt's First Theorem and Frugality of Deep Learning.Entropy (Basel, Switzerland) · 2022Article
- A Fast kNN Algorithm Using Multiple Space-Filling Curves.Entropy (Basel, Switzerland) · 2022Article
- Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation.Entropy (Basel, Switzerland) · 2021Article
- Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning.Entropy (Basel, Switzerland) · 2021Article
- Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge observational clinical datasets are becoming increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete disease state develops through stereotypical routes, characterized by "points of no return" and "final states" (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow-up) observations.
resultsHere we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs, which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection, and quantifying the geodesic distances (pseudo-time) in partially ordered sequences of observations. The methodology allows a patient to be positioned on a particular clinical trajectory (pathological scenario) and the degree of progression along it to be characterized with a qualitative estimate of the uncertainty of the prognosis. We developed a tool ClinTrajan for clinical trajectory analysis implemented in the Python programming language. We test the methodology in 2 large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data.
conclusionsOur pseudo-time quantification-based approach makes it possible to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data.
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