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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.

Papers
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Proceedings ArticleDOI

Abstract PR04: A melanoma transcriptional state distinction influences sensitivity to MAPK pathway inhibitors

TL;DR: It is shown that RAF inhibitor sensitive and resistant BRAF V600 -mutant melanomas display distinct transcriptional profiles that suggest the transcriptional context in which an oncogenic event arises can have a profound impact on the establishment of oncogene-dependencies and associated drug susceptibilities.
Journal ArticleDOI

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.
Proceedings ArticleDOI

Abstract 1310: Systematic evaluation of gene networks for discovery of disease genes

TL;DR: A parsimonious composite network with both high efficiency and absolute performance, which outperforms any single resource is created, which provides a benchmark for selection of molecular networks in human disease research.
Patent

Method and device for analyzing gene expression data

TL;DR: In this paper, a method for grouping plural data points which are a series of gene expression values, respectively, in a computer system comprises a step for receiving the gene expression value of the data points, a step to selecting any data point exhibiting a non-prominent change in the expression values so as to leave working data points and a step by grouping the data point expressing similar patterns are grouped into clusters, respectively.