ArticleScientific reports2021
Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model.
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 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Integrated Bioinformatics and Experimental Validation to Identify a Disulfidptosis-Related lncRNA Model for Prognostic Prediction in Papillary Renal Cell Carcinoma.Combinatorial chemistry & high throughput screening · 2025Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- NF-YAl drives EMT in ClaudinCell death & disease · 2023Article
- The expression profile of Gasdermin C-related genes predicts the prognosis and immunotherapy response of pancreatic adenocarcinoma.American journal of cancer research · 2023Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Pan-cancer classification by regularized multi-task learning.Scientific reports · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease's progression, helping the disease's etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.
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