ArticleTheScientificWorldJournal2012
Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods.
Article in TheScientificWorldJournal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 19 citations in OpenAlex.
- Multi-transcriptomics predicts clinical outcome in systemically untreated breast cancer patients with extensive follow-up.Breast cancer research : BCR · 2025Article
- Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases.Cancer medicine · 2024Article
- Article
- Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization.BMC bioinformatics · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma.JAMA network open · 2022Article
- Article
- Machine learning and deep learning methods that use omics data for metastasis prediction.Computational and structural biotechnology journal · 2021Review
- Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data.Frontiers in genetics · 2019Article
- Long non-coding RNA expression profiles predict metastasis in lymph node-negative breast cancer independently of traditional prognostic markers.Breast cancer research : BCR · 2015Article
- Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.TheScientificWorldJournal · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.
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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.