Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics

scientific article

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1016/S0079-6123(06)58004-5
P698PubMed publication ID17027692

P2093author name stringJohn H Phan
May D Wang
Chang-Feng Quo
P2860cites workOn peptide de novo sequencing: a new approachQ51634712
Multivariate approach for selecting sets of differentially expressed genesQ52045598
Nearest neighbor pattern classificationQ56219830
Gene Selection for Cancer Classification using Support Vector MachinesQ56535529
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Note on Free Lunches and Cross-ValidationQ57830284
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Cluster analysis and display of genome-wide expression patternsQ24644463
GoMiner: a resource for biological interpretation of genomic and proteomic dataQ24796534
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Predictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signaturesQ24812541
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Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variationQ27860759
Quantitative monitoring of gene expression patterns with a complementary DNA microarrayQ27861102
Mass spectrometry-based proteomicsQ28182890
Locally Weighted Regression: An Approach to Regression Analysis by Local FittingQ29393395
Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiationQ29616072
High density synthetic oligonucleotide arraysQ29616560
Computational analysis of microarray dataQ29618377
Centralization: a new method for the normalization of gene expression data.Q30657303
Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression dataQ30669156
Variable selection and pattern recognition with gene expression data generated by the microarray technology.Q30676405
Deriving quantitative conclusions from microarray expression dataQ30704356
Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectraQ30868895
Gene selection for microarray data analysis using principal component analysisQ30986340
Proteomics to study genes and genomesQ33906818
Control selection for RNA quantitationQ34007791
The ABC's (and XYZ's) of peptide sequencingQ34344848
The basics of mass spectrometry in the twenty-first centuryQ35058569
Protein microarrays: molecular profiling technologies for clinical specimensQ35574627
The curse of normalizationQ36750544
Proteomics. Proteomics in genomelandQ40721183
Intensity-based protein identification by machine learning from a library of tandem mass spectra.Q45966875
Is cross-validation valid for small-sample microarray classification?Q47207899
Multiclass cancer classification and biomarker discovery using GA-based algorithmsQ47619196
Estimating misclassification error with small samples via bootstrap cross-validationQ48499637
A graph-theoretic approach for the separation of b and y ions in tandem mass spectraQ48521125
Automatic quality assessment of peptide tandem mass spectra.Q48534080
Robust PCA and classification in biosciencesQ48557500
Statistical process control for large scale microarray experimentsQ48625692
P921main subjectdata miningQ172491
genomicsQ222046
functional genomicsQ1068690
bioinformaticsQ128570
P304page(s)83-108
P577publication date2006-01-01
P13046publication type of scholarly workreview articleQ7318358
P1433published inProgress in Brain ResearchQ15800382
P1476titleFunctional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics
P478volume158

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cites work (P2860)
Q38644393A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression
Q30572427A repository based on a dynamically extensible data model supporting multidisciplinary research in neuroscience
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Q41680893Toxicity prediction from toxicogenomic data based on class association rule mining

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