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Review
. 2007 Feb 23;3(2):e12.
doi: 10.1371/journal.pcbi.0030012.

From bytes to bedside: data integration and computational biology for translational cancer research

Jomol P Mathew  1 , Barry S Taylor, Gary D Bader, Saiju Pyarajan, Marco Antoniotti, Arul M Chinnaiyan, Chris Sander, Steven J Burakoff, Bud Mishra
Affiliations
Review

From bytes to bedside: data integration and computational biology for translational cancer research

Jomol P Mathew et al. PLoS Comput Biol. .
No abstract available

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Conflict of interest statement (V体育安卓版)

Competing interests. The authors have declared that no competing interests exist VSports手机版.

"VSports app下载" Figures

Figure 1
Figure 1. Data Integration for Translational Cancer Research
Archival of clinical and molecular data in easily retrievable standardized formats, aggregation, integration, and data analysis will provide opportunities for the next-generation biomedical discoveries that can impact cancer research and treatment.
Figure 2
Figure 2. Protein Profiling for Cancer Diagnosis and Prognosis
Generation of protein profiles using mass spectrometry is an example of an experimental technique that produces massive amounts of data that is difficult to interpret without computational and statistical algorithms. For instance, comparison of disease versus control sample profiles can lead to identification of disease-specific protein expression signatures, which could be used as diagnostic or prognostic markers. Aggregation of such data from multiple sources and pooled analysis requires proper annotation of sample source, sample handling, and experiment information.
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