Download Machine Learning Methods Applied to DNA Microarray Data Can

Machine Learning Methods Applied to DNA Microarray
Data Can Improve the Diagnosis of Cancer
Eric Bair
Robert Tibshirani
Dept. of Statistics
Stanford University
Stanford, CA 94305-4065
Depts. of Health, Research, & Policy, and
Statistics
Stanford University
Stanford, CA 94305-4065
ebair@stanford.edu
tibs@stat.stanford.edu
ABSTRACT
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SIGKDD Explorations.
Volume 5,Issue 2 - Page 51
BL EWS NB RMS
813841
859359
207274
296448
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486110
ïtissue plasminogen activator
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í
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ñhomolog of mouse mesoderm specific transcript
growth factor receptor 4
öfibroblast
sarcoglycan alpha (dystrophin-associated glycoprotein)
èEST
presenilin 2 (Alzheimer disease 4)
ïtroponin T2, cardiac muscle isoforms
îmyosin MYL4
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õ
amidinotransferase
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neurofibromin 2 (mutated in neurofibromatosis type 2)
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farnesyltransferase 1
êfarnesyl-diphosphate
growth associated protein 43 (GAP43)
ô
óN-cadherin (neuronal)
gene from chromosome 1q
îALL1-fused
meningioma 1 (disrupted in balanced translocation)
ëcold shock domain protein A
òneuroblastoma protein (NOE1)
ñFas-associated protein tyrosine phosphatase 1
lymphoma variant translocation protein 1
êfollicular
glycogenin 2
ð
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MIC2 surface antigen (CD99)
Fc fragment receptor transporter, alpha chain
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caveolin 1 (caveolae protein)
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EST
factor 1 (ets domain transcription factor)
îE74-like
major histocompatibility complex, class II, DQ alpha 1
îmajor histocompatibility complex, class II, DM alpha
í
growth factor binding protein 2
ìinsulin-like
receptor type protein tyrosine phosphatase F
éantiquitin 1
êglutathione S-transferase A4
ëcDNA DKFZp586J2118
êgrowth arrest-specific protein 1
EST
éEST
alpha 1 catenin (cadherin-associated protein)
èprofilin 2
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Volume 5,Issue 2 - Page 52
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DIAGNOSIS OF CANCERS WHERE NO
SUBTYPES ARE KNOWN TO EXIST
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8. REFERENCES
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Training Data
Probability
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SIGKDD Explorations.
Volume 5,Issue 2 - Page 54
1.0
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Volume 5,Issue 2 - Page 55