ArticleFrontiers in genetics2019
Gene Expression Value Prediction Based on XGBoost Algorithm.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.
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Who cites it
104 citing papers in PubMed, 1 synthesis or guideline pooled it, 319 citations in OpenAlex.
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- Unraveling the drivers of leptospirosis risk in Thailand using machine learning.PLoS neglected tropical diseases · 2025Article
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- Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes.Briefings in bioinformatics · 2025Article
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- MEA-seqX: High-Resolution Profiling of Large-Scale Electrophysiological and Transcriptional Network Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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44 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression profiling has been widely used to characterize cell status to reflect the health of the body, to diagnose genetic diseases, etc. In recent years, although the cost of genome-wide expression profiling is gradually decreasing, the cost of collecting expression profiles for thousands of genes is still very high. Considering gene expressions are usually highly correlated in humans, the expression values of the remaining target genes can be predicted by analyzing the values of 943 landmark genes. Hence, we designed an algorithm for predicting gene expression values based on XGBoost, which integrates multiple tree models and has stronger interpretability. We tested the performance of XGBoost model on the GEO dataset and RNA-seq dataset and compared the result with other existing models. Experiments showed that the XGBoost model achieved a significantly lower overall error than the existing D-GEX algorithm, linear regression, and KNN methods. In conclusion, the XGBoost algorithm outperforms existing models and will be a significant contribution to the toolbox for gene expression value prediction.
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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.