scispace - formally typeset
P

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
More filters
Journal ArticleDOI

Role of Tet1/3 Genes and Chromatin Remodeling Genes in Cerebellar Circuit Formation

TL;DR: A role for Tet genes and chromatin remodeling genes in the formation of cerebellar circuitry is demonstrated using metagene analysis and it is demonstrated that Knockdown of Tet1 and Tet3 by RNAi in ex vivo cerebellary slice cultures inhibits dendritic arborization of developing GCs, a critical step in circuit formation.
Journal ArticleDOI

An Analytical Method for Multiclass Molecular Cancer Classification

TL;DR: In this paper, a computational methodology for multiclass prediction that combines class-specific (one vs. all) binary support vector machines was proposed for the diagnosis of multiple common adult malignancies using DNA microarray data.
Journal ArticleDOI

An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity

TL;DR: A transcriptional signature from the peripheral blood of MS patients may be able to help identify individuals who are more likely to relapse when treated with first-line MS drugs and could become part of a tool to help neurologists identify those MS patients at higher risk of attacks.
Journal ArticleDOI

Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding

TL;DR: Cl similarity weighted nonnegative embedding (SWNE) is developed, which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types.