From bytes to bedside: data integration and computational biology for translational cancer research
about
Data integration in biological research: an overviewBioinformatics for cancer immunology and immunotherapyRequirements for data integration platforms in biomedical research networks: a reference modelSystems integration of biodefense omics data for analysis of pathogen-host interactions and identification of potential targetsProtein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data.Predicting cancer involvement of genes from heterogeneous data.OncDRS: An integrative clinical and genomic data platform for enabling translational research and precision medicineA kernel-based integration of genome-wide data for clinical decision support.Mathematical modeling of molecular data in translational medicine: theoretical considerations.The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvementPathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data.The structural basis for cancer treatment decisions.Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.Pseudonymization of patient identifiers for translational research.Mature T cell responses are controlled by microRNA-142.Network structure and the role of key players in a translational cancer research network: a study protocolA novel information retrieval model for high-throughput molecular medicine modalities.Allogeneic T cell responses are regulated by a specific miRNA-mRNA network.Leadership in complex networks: the importance of network position and strategic action in a translational cancer research network.Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference.From bench to bedside: the growing use of translational research in cancer medicine.Bioinformatics approaches in the discovery and understanding of reproduction-related biomarkers.Emerging antibody combinations in oncology.Tools for protein-protein interaction network analysis in cancer research.Protein-protein interaction networks studies and importance of 3D structure knowledge.Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization method.SAG/Rbx2-Dependent Neddylation Regulates T-Cell Responses.
P2860
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P2860
From bytes to bedside: data integration and computational biology for translational cancer research
description
2007 nî lūn-bûn
@nan
2007 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
From bytes to bedside: data in ...... translational cancer research
@ast
From bytes to bedside: data in ...... translational cancer research
@en
From bytes to bedside: data in ...... translational cancer research
@en-gb
From bytes to bedside: data in ...... translational cancer research
@nl
type
label
From bytes to bedside: data in ...... translational cancer research
@ast
From bytes to bedside: data in ...... translational cancer research
@en
From bytes to bedside: data in ...... translational cancer research
@en-gb
From bytes to bedside: data in ...... translational cancer research
@nl
prefLabel
From bytes to bedside: data in ...... translational cancer research
@ast
From bytes to bedside: data in ...... translational cancer research
@en
From bytes to bedside: data in ...... translational cancer research
@en-gb
From bytes to bedside: data in ...... translational cancer research
@nl
P2093
P2860
P50
P921
P1476
From bytes to bedside: data in ...... translational cancer research
@en
P2093
Arul M Chinnaiyan
Barry S Taylor
Bud Mishra
Jomol P Mathew
Marco Antoniotti
Saiju Pyarajan
P2860
P356
10.1371/JOURNAL.PCBI.0030012
P407
P5008
P577
2007-02-23T00:00:00Z