ArticleScientific reports2016
A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis.Disease markers · 2022Trial
- Harnessing the Power of AI for Enhanced Diagnosis and Treatment of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Review
- Article
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- Emerging computational tools and models for studying gut microbiota composition and function.Current opinion in biotechnology · 2020Review
- Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors.Hepatology communications · 2020Article
- Article
- Juice Index: an integrated Sauvignon blanc grape and wine metabolomics database shows mainly seasonal differences.Metabolomics : Official journal of the Metabolomic Society · 2019Article
- Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition.Scientific reports · 2018Article
- Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation.Cancer management and research · 2018Review
- Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation.PloS one · 2018Article
- A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.Scientific reports · 2017Article
- Extracellular Microbial Metabolomics: The State of the Art.Metabolites · 2017Review
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
Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.
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.