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Pablo Tamayo

Researcher at University of California, San Diego

Publications -  185
Citations -  117545

Pablo Tamayo is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Cancer & Gene. The author has an hindex of 72, co-authored 177 publications receiving 97318 citations. Previous affiliations of Pablo Tamayo include University of California, Berkeley & Harvard University.

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The limitations of simple gene set enrichment analysis assuming gene independence

TL;DR: The results provide strong empirical evidence that gene–gene correlations cannot be ignored due to the significant variance inflation they produced on the enrichment scores and should be taken into account when estimating gene set enrichment significance.
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Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

TL;DR: It is shown how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification.
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Cytometric profiling in multiple sclerosis uncovers patient population structure and a reduction of CD8low cells

TL;DR: Large-scale immunophenotyping approach has yielded robust evidence for a reduction of CD8(low)CD4(-) cells in both CIS and RRMS in the absence of treatment as well as suggestive evidence for the existence of immunologically distinct subsets of subjects with a demyelinating disease.
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Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

TL;DR: These studies identify TLOC1 and SKIL as driver genes in 3q26 and suggest that cooperating genes may be coamplified in other regions with somatic copy number gain, providing evidence that regions of somaticcopy number gain may harbor cooperating genes of different but complementary functions.
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Combining Gene Expression Profiles and Clinical Parameters for Risk Stratification in Medulloblastomas

TL;DR: Gene expression profiling predicts medulloblastoma outcome independent of clinical variables, and univariate analysis demonstrated expression profiles to be the only significant clinical prognostic factor.