ArticleBMC bioinformatics2022
Assessment of deep learning and transfer learning for cancer prediction based on gene expression data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques.PloS one · 2026Article
- Cross-Cancer Transfer Learning for Gastric Cancer Risk Prediction from Electronic Health Records.Diagnostics (Basel, Switzerland) · 2025Article
- Self-supervised representation learning on gene expression data.Bioinformatics (Oxford, England) · 2025Article
- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond.Nature communications · 2025Article
- Identification and validation of a 9-RBPs-related gene signature associated with prognosis and immune infiltration in bladder cancer based on bioinformatics analysis and machine learning.Translational andrology and urology · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- A comparative analysis of gene expression profiling by statistical and machine learning approaches.Bioinformatics advances · 2025Article
- Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.IEEE access : practical innovations, open solutions · 2025Article
- Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Observational
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Deep learning-based multimodal spatial transcriptomics analysis for cancer.Advances in cancer research · 2024Review
- Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.Journal of biological methods · 2024Review
- From genetic correlations of Alzheimer's disease to classification with artificial neural network models.Functional & integrative genomics · 2023Article
- AttOmics: attention-based architecture for diagnosis and prognosis from omics data.Bioinformatics (Oxford, England) · 2023Article
- Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings.La Radiologia medica · 2023Article
- Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning is now a standard tool for cancer prediction based on gene expression data. However, deep learning is still new for this task, and there is no clear consensus about its performance and utility. Few experimental works have evaluated deep neural networks and compared them with state-of-the-art machine learning. Moreover, their conclusions are not consistent.
resultsWe extensively evaluate the deep learning approach on 22 cancer prediction tasks based on gene expression data. We measure the impact of the main hyper-parameters and compare the performances of neural networks with the state-of-the-art. We also investigate the effectiveness of several transfer learning schemes in different experimental setups.
conclusionBased on our experimentations, we provide several recommendations to optimize the construction and training of a neural network model. We show that neural networks outperform the state-of-the-art methods only for very large training set size. For a small training set, we show that transfer learning is possible and may strongly improve the model performance in some cases.
Indexed as
Identifiers
What Socratic 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 Socratic graph.