ReviewBioData mining2017
Computational dynamic approaches for temporal omics data with applications to systems medicine.
Review in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- lagCI enables inference of temporal causal relationships from dense multi-omic time series.Briefings in bioinformatics · 2026Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Anomaly Detection with Graph Embedding in Bioinformatics: A Survey.Current genomics · 2025Review
- Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.mSystems · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
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- The burden of inappropriate birth weight on neonatal survival in term newborns: a population-based study in a middle-income setting.Frontiers in pediatrics · 2023Article
- Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis.BioData mining · 2021Article
- Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research.Frontiers in genetics · 2021Article
- A Simple Toxicokinetic Model Exhibiting Complex Dynamics and Nonlinear Exposure Response.Risk analysis : an official publication of the Society for Risk Analysis · 2020Article
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- eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.PLoS computational biology · 2020Article
- Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases.Frontiers in genetics · 2019Review
- Article
- CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.PLoS computational biology · 2018Article
- Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.Metabolites · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.
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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.