@article{TCR2645,
author = {Dung-Tsa Chen and Yian Ann Chen},
title = {Statistical and bioinformatics applications in biomedical omics research},
journal = {Translational Cancer Research},
volume = {3},
number = {3},
year = {2014},
keywords = {},
abstract = {From the initiation of human genome project in 1990, and announcement of its completion in 2003, to the publications of first drafts of human proteome in Nature on May 29, 2014, these omics research projects have not only revolutionized the scientific research and clinical practices, but also have profound influence on agriculture, renewable energy development, biotechnology, and many other disciplines. Rapid advancements in high-throughput molecular technologies in the past several decades have enabled the identification and profiling of high-dimensional omics data, including DNA/RNA sequencing, RNA expression, methylation, DNA copy number variation, metabolomics, proteomics, and post-translational modification data. It is by no means a trivial task to analyze these Big Data generated by different platforms since there are various complex computational and analytical issues associated with them. The field of Biostatistics and Bioinformatics has played an essential role in solving computational issues and making sense of these data so that the research findings could lead to better understanding of human health, improve disease diagnosis, and provide opportunities for personalized treatment for complex human diseases such as cancer.},
issn = {2219-6803}, url = {https://tcr.amegroups.org/article/view/2645}
}