ArticleNature medicine2001
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks.
Article in Nature medicine, 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 489 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
489 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes.Biomolecules · 2023Pooled it
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Explainable AI: A review of applications to neuroimaging data.Frontiers in neuroscience · 2022Pooled it
- A Robust Masked Painter Framework for Gene Selection in Binary Classification of High-Dimensional Functional Genomic Data.Entropy (Basel, Switzerland) · 2026Article
- Targeted antibody-drug conjugates for rhabdomyosarcoma and other FGFR4-expressing cancers.Cell reports. Medicine · 2026Article
- Topology constrained nonnegative matrix factorization for time varying omic expression.Scientific reports · 2026Article
- CAR-T cell therapy for pediatric solid tumors: armored CAR-T cells and beyond.Cancer metastasis reviews · 2026Review
- CAR-T cell immunotherapy in rhabdomyosarcoma.Journal of translational medicine · 2026Review
- Clinical management and therapeutic development for the rare disease rhabdomyosarcoma.Journal of Cancer · 2026Review
- Hybrid Gene Selection Algorithm for Cancer Classification Using Nuclear Reaction Optimization (NRO).Current issues in molecular biology · 2025Article
- GNR: Genetic-Embedded Nuclear Reaction Optimization with F-Score Filter for Gene Selection in Cancer Classification.International journal of molecular sciences · 2025Article
- Sparse vertex discriminant analysis: Variable selection for biomedical classification applications.Computational statistics & data analysis · 2025Article
- Explainable AI Model Reveals Informative Mutational Signatures for Cancer-Type Classification.Cancers · 2025Article
- Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations.Entropy (Basel, Switzerland) · 2025Article
- MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.BMC bioinformatics · 2025Article
- Univariate-Guided Sparse Regression.Harvard data science review · 2025Article
- Interfered feature elimination coupled with feature group selection for wound infection detection by electronic nose.PloS one · 2025Article
- Fusion oncogenes in rhabdomyosarcoma: model systems, mechanisms of tumorigenesis, and therapeutic implications.Frontiers in oncology · 2025Review
- Current landscape of CAR-therapy for osteosarcoma and rhabdomyosarcoma.Frontiers in immunology · 2025Review
429 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.
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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.