Bioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data

scientific article published on 28 May 2009

Bioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1155/2009/892863
P932PMC publication ID2688763
P698PubMed publication ID19704920
P5875ResearchGate publication ID26765036

P50authorIssam El NaqaQ57612481
Joseph DeasyQ41453915
Sarah SpencerQ47502360
P2093author name stringJeffrey D Bradley
Damian Almiron Bonnin
P2860cites workGene Selection for Cancer Classification using Support Vector MachinesQ56535529
Use of principal component analysis to evaluate the partial organ tolerance of normal tissues to radiationQ57619560
The Linear-Quadratic Model Is Inappropriate to Model High Dose per Fraction Effects in RadiosurgeryQ57730424
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Dosimetric predictors of radiation-induced lung injuryQ74816555
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Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factorsQ47601234
Comparison of the predicted and observed secondary structure of T4 phage lysozymeQ29398301
Fitting tumor control probability models to biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer: pitfalls in deducing radiobiologic parameters for tumors from clinical dataQ30665424
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Analysis and validation of proteomic data generated by tandem mass spectrometryQ31131394
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Non-small cell lung cancer therapy-related pulmonary toxicity: an update on radiation pneumonitis and fibrosisQ36193567
P275copyright licenseCreative Commons Attribution 3.0 UnportedQ14947546
P6216copyright statuscopyrightedQ50423863
P921main subjectbioinformaticsQ128570
P304page(s)892863
P577publication date2009-05-28
P1433published inJournal of Biomedicine and BiotechnologyQ15752146
P1476titleBioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data
P478volume2009

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cites work (P2860)
Q33791102A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data
Q36824401Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning
Q40934956Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk
Q36578229Bioinformatics insights into acute lung injury/acute respiratory distress syndrome
Q33698936Predicting radiotherapy outcomes using statistical learning techniques.
Q38078863Serum and Plasma Proteomics and Its Possible Use as Detector and Predictor of Radiation Diseases

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