ArticleBMC bioinformatics2018
A Bayesian approach to determine the composition of heterogeneous cancer tissue.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- Risk prediction of postoperative acute kidney injury in patients undergoing off-pump coronary artery bypass grafting based on machine learning.The Journal of international medical research · 2026Article
- Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients.Reviews in cardiovascular medicine · 2025Article
- Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study.International journal of general medicine · 2023Article
- Article
- Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.International journal of general medicine · 2023Article
- Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME.Frontiers in surgery · 2023Article
- Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC).BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue.
methodsIn this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework.
resultsThe algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma).
conclusionThe algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.
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