Your browser doesn't support javascript.
loading
ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.
Shen, Ying; Rahman, Mumtahena; Piccolo, Stephen R; Gusenleitner, Daniel; El-Chaar, Nader N; Cheng, Luis; Monti, Stefano; Bild, Andrea H; Johnson, W Evan.
Afiliación
  • Shen Y; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • Rahman M; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • Piccolo SR; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of M
  • Gusenleitner D; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • El-Chaar NN; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • Cheng L; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • Monti S; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
  • Bild AH; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of M
  • Johnson WE; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of M
Bioinformatics ; 31(11): 1745-53, 2015 Jun 01.
Article en En | MEDLINE | ID: mdl-25617415
MOTIVATION: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics. RESULTS: Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens. AVAILABILITY AND IMPLEMENTATION: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Perfilación de la Expresión Génica Tipo de estudio: Guideline / Prognostic_studies Límite: Animals / Female / Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Perfilación de la Expresión Génica Tipo de estudio: Guideline / Prognostic_studies Límite: Animals / Female / Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2015 Tipo del documento: Article Pais de publicación: Reino Unido