Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.
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ExSTraCS 2.0: Description and Evaluation of a Scalable Learning Classifier System.A Multi-Core Parallelization Strategy for Statistical Significance Testing in Learning Classifier Systems.Machine learning applications in cancer prognosis and predictionThe cAMP responsive element binding protein 1 transactivates epithelial membrane protein 2, a potential tumor suppressor in the urinary bladder urothelial carcinomaMethod for generating multiple risky barcodes of complex diseases using ant colony algorithm.Functional networks inference from rule-based machine learning models.A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.
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P2860
Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach.
description
2013 nî lūn-bûn
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Role of genetic heterogeneity ...... ng classifier system approach.
@en
Role of genetic heterogeneity ...... ng classifier system approach.
@nl
type
label
Role of genetic heterogeneity ...... ng classifier system approach.
@en
Role of genetic heterogeneity ...... ng classifier system approach.
@nl
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Role of genetic heterogeneity ...... ng classifier system approach.
@en
Role of genetic heterogeneity ...... ng classifier system approach.
@nl
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P2860
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Role of genetic heterogeneity ...... ng classifier system approach.
@en
P2093
Angeline S Andrew
Margaret Rita Karagas
Ryan John Urbanowicz
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P304
P356
10.1136/AMIAJNL-2012-001574
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2013-02-26T00:00:00Z