ArticleNucleic acids research2017
Computational approach for deriving cancer progression roadmaps from static sample data.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Identification of a novel immune-related gene signature by single-cell and bulk sequencing for the prediction of the immune landscape and prognosis of breast cancer.Cancer cell international · 2024Article
- Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States.Cancer research communications · 2024Article
- Cancerous time estimation for interpreting the evolution of lung adenocarcinoma.Briefings in bioinformatics · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
- Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression.PLoS computational biology · 2023Article
- Article
- An Analysis of Transcriptomic Burden Identifies Biological Progression Roadmaps for Hematological Malignancies and Solid Tumors.Biomedicines · 2022Article
- Deep learning approach for cancer subtype classification using high-dimensional gene expression data.BMC bioinformatics · 2022Article
- Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.Genes · 2021Article
- Identification of three immune molecular subtypes associated with immune profiles, immune checkpoints, and clinical outcome in multiple myeloma.Cancer medicine · 2021Article
- Computational disease progression modeling can provide insights into cancer evolution.Oncoscience · 2020Article
- Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.Bioinformatics (Oxford, England) · 2020Article
- Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer.Cancer research · 2020Article
- SENSE: Siamese neural network for sequence embedding and alignment-free comparison.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.
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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.