ArticleGenes2018
Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.IET systems biology · 2024Article
- Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis.BMC cardiovascular disorders · 2024Article
- A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data.Scientific reports · 2023Article
- Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome.PloS one · 2023Article
- Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques.Journal of inflammation research · 2023Article
- Artificial intelligence in clinical research of cancers.Briefings in bioinformatics · 2022Article
- Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest.Frontiers in genetics · 2022Article
- Article
- Machine Learning in Healthcare.Current genomics · 2021Review
- Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor.Oncology reports · 2020Article
- Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.Cancer gene therapy · 2020Article
- The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method.Frontiers in genetics · 2020Article
- Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.Frontiers in physiology · 2020Article
- Merging microarray studies to identify a common gene expression signature to several structural heart diseases.BioData mining · 2020Article
- The Blood Gene Expression Signature for Kawasaki Disease in Children Identified with Advanced Feature Selection Methods.BioMed research international · 2020Article
- Investigation and Prediction of Human Interactome Based on Quantitative Features.Frontiers in bioengineering and biotechnology · 2020Article
- Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network.BioMed research international · 2020Article
- Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data.Frontiers in genetics · 2020Article
- The Methylation Pattern for Knee and Hip Osteoarthritis.Frontiers in cell and developmental biology · 2020Article
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
Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.
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.