Characterizing Cancer-Specific Networks by Integrating TCGA Data

Characterizing Cancer-Specific Networks by Integrating TCGA Data

The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a B...

Big linked cancer data: Integrating linked TCGA and PubMed

The amount of bio-medical data available on the Web grows exponentially with time. The resulting large volume of data makes manual exploration very tedious. Moreover, the velocity at which this data changes and the variety of formats in which bio-medical data is published makes it difficult to access them in an integrated form. Finally, the lack of an integrated vocabulary makes querying this d...

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

BACKGROUND Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate th...

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aracne.networks, a data package containing gene regulatory networks assembled from TCGA data by the ARACNe algorithm

ARACNe networks This package contains 24 Mutual Information-based networks assembled by ARACNeAP [1] with default parameters (MI p-value = 10−8, 100 bootstraps and permutation seed = 1). ARACNe is a network inference algorithm based on an Adaptive Partioning (AP) Mutual Information (MI) approach [1]. In short, ARACNe-AP estimates all pairwise Mutual Information scores between gene expression pr...