ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Effect of data combination on predictive modeling: a study using gene expression data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients.Briefings in bioinformatics · 2016Pooled it
- VERTIcal Grid lOgistic regression (VERTIGO).Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Selecting cases for whom additional tests can improve prognostication.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.PloS one · 2012Article
- I-spline Smoothing for Calibrating Predictive Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012Article
- Smooth isotonic regression: a new method to calibrate predictive models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2011Article
- Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe quality of predictive modeling in biomedicine depends on the amount of data available for model building.
objectiveTo study the effect of combining microarray data sets on feature selection and predictive modeling performance.
methodsEmpirical evaluation of stability of feature selection and discriminatory power of classifiers using three previously published gene expression data sets, analyzed both individually and in combination.
resultsFeature selection was not robust for the individual as well as for the combined data sets. The classification performance of models built on individual and combined data sets was heavily dependent on the data set from which the features were extracted.
conclusionWe identified volatility of feature selection as contributing factor to some of the problems faced by predictive modeling using microarray data.
Indexed as
Identifiers
21347042PMC3041313What 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.