ArticleScientific reports2018
A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- Trial
- Machine learning-driven decoding of maternal immune signatures in repeated pregnancy loss.PLoS computational biology · 2026Article
- Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.European heart journal. Digital health · 2026Article
- Initial Physiological and Molecular Adjustments Underpin Salinity Tolerance During Wheat Germination and Early Seedling Development.Plants (Basel, Switzerland) · 2026Article
- Hybrid Graph-Machine Learning Framework for Accurate and Interpretable Band Gap Prediction.Journal of chemical information and modeling · 2026Article
- Diagnosing migraine from genome-wide genotype data: a machine learning analysis.Brain : a journal of neurology · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy.Foods (Basel, Switzerland) · 2025Article
- Analysis of influencing factors and equity in health education for mobile populations based on random forest model: evidence from the China migrants dynamic survey.Frontiers in public health · 2025Article
- Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.Forestry research · 2025Article
- Analysis of influencing factors and equity in hospitalization expense reimbursement for mobile populations based on random forest model: a cross-sectional study from China.Frontiers in public health · 2025Article
- CAR-T therapy-based innovations in the enhancement of contemporary anti-tumor therapies.Frontiers in immunology · 2025Review
- An Overview of Software Sensor Applications in Biosystem Monitoring and Control.Sensors (Basel, Switzerland) · 2024Review
- Superior electroadhesion force with permittivity-engineered bilayer films using electrostatic simulation and machine learning approaches.Scientific reports · 2024Article
- Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation.Journal of clinical medicine · 2024Article
- Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.Frontiers in bioinformatics · 2024Article
- TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.PloS one · 2024Article
- Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.PeerJ. Computer science · 2024Article
- Hybrid deep learning approach to improve classification of low-volume high-dimensional data.BMC bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In predictive model development, gene expression data is associated with the unique challenge that the number of samples (n) is much smaller than the amount of features (p). This "n ≪ p" property has prevented classification of gene expression data from deep learning techniques, which have been proved powerful under "n > p" scenarios in other application fields, such as image classification. Further, the sparsity of effective features with unknown correlation structures in gene expression profiles brings more challenges for classification tasks. To tackle these problems, we propose a newly developed classifier named Forest Deep Neural Network (fDNN), to integrate the deep neural network architecture with a supervised forest feature detector. Using this built-in feature detector, the method is able to learn sparse feature representations and feed the representations into a neural network to mitigate the overfitting problem. Simulation experiments and real data analyses using two RNA-seq expression datasets are conducted to evaluate fDNN's capability. The method is demonstrated a useful addition to current predictive models with better classification performance and more meaningful selected features compared to ordinary random forests and deep neural networks.
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.