ArticlePLoS computational biology2021
Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Acetyl-CoA synthase 1 mediates metabolic reprogramming to promote proliferation and metastasis of osteosarcoma.Journal of orthopaedic translation · 2026Article
- CTAS: a network control theory-based approach to identify key regulatory TFs of AS events during epithelial-mesenchymal transition.Briefings in bioinformatics · 2026Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- Baseline atherogenic index of plasma and its trajectory predict onset of type 2 diabetes in a health screened adult population: a large longitudinal study.Cardiovascular diabetology · 2025Article
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
- Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma.Liver research (Beijing, China) · 2024Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- CSER: a gene regulatory network construction method based on causal strength and ensemble regression.Frontiers in genetics · 2024Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Research square · 2023Article
- PCB: A pseudotemporal causality-based Bayesian approach to identify EMT-associated regulatory relationships of AS events and RBPs during breast cancer progression.PLoS computational biology · 2023Article
- Multiscale modeling of drug resistance in glioblastoma with gene mutations and angiogenesis.Computational and structural biotechnology journal · 2023Article
- Article
- Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories.Briefings in functional genomics · 2022Article
- An Analysis of Transcriptomic Burden Identifies Biological Progression Roadmaps for Hematological Malignancies and Solid Tumors.Biomedicines · 2022Article
- Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R.STAR protocols · 2022Article
- Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.
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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.