Putative biomarkers for predicting tumor sample purity based on gene expression data
Abstract Background Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-canc...
Main Authors: | Yuanyuan Li, David M. Umbach, Adrienna Bingham, Qi-Jing Li, Yuan Zhuang, Leping Li |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-12-01
|
Series: | BMC Genomics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12864-019-6412-8 |
Similar Items
-
Combining K-Means and XGBoost Models for Anomaly Detection Using Log Datasets
by: João Henriques, et al.
Published: (2020-07-01) -
Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms
by: Weizhang Liang, et al.
Published: (2020-05-01) -
Two Derivative Algorithms of Gradient Boosting Decision Tree for Silicon Content in Blast Furnace System Prediction
by: Shihua Luo, et al.
Published: (2020-01-01) -
Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081)
by: Amita Sharma, et al.
Published: (2020-04-01) -
Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm
by: Marco Febriadi Kokasih, et al.
Published: (2020-09-01)