Guest Editors: Dung-Tsa Chen and Ann Chen, Moffitt Cancer Center & Research Institute, Tampa, USA
Advances in high-throughput molecular technologies have provided new insights into medicine. It is hoped that these insights will lead to better diagnosis or treatment of complex diseases such as cancer. In this special issue planned to be published in June Issue of 2014, we include articles introducing statistical methods and bioinformatics tools, which have been developed to address different aspects of computationally challenging issues including high-dimensionality, variability of high-throughput platforms, pattern discovery, data integration, reverse engineering to gain mechanistic insight, or deriving classifiers for improving diagnosis or guiding patient therapeutic options. The following is the outline of this issue.
- Preface (Guest Editors)
- Integrating network topology in genomics data analysis (Hongyu Zhao, Yale School of Public Health, USA)
- Identification of mechanisms in biological systems through reverse engineering (Eberhard O. Voit and Zhen Qi, Georgia Institute of Technology, USA)
- An understanding of cancer genomics through massively parallel sequencing (Jamie Teer, H. Lee Moffitt Cancer Center and Research Institute, USA)
- Integrative Clustering Methods for Genomic Data (Prabhakar Chalise, University of Kansas Medical Center, USA)
- A recursively partitioned mixture model for clustering time-course gene expression data (Devin Koestler, University of Kansas Medical Center, USA)
- Statistical Strategies for RNAseq Batch Effect Reduction (Yu Shyr, Yan Guo, and Fei Ye, Vanderbilt University Medical Center, USA)
- Computational tools and statistical models for phosphorylation signaling inference (Y. Ann Chen and Steven Eschrich, Moffitt Cancer Center & Research Institute, USA)
- Sparse Principal Component Analysis in Cancer Research (Po-Yu Huang and Ying-Lin Hsu, National Chiao Tung University, Taiwan)
- Adaptive gene signature design (James Chen, National Center for Toxicological Research, Food and Drug Administration, USA)
- Applications of Artificial Neural Networks in Cancer Genomics: Diagnosis, Prognosis, and Prediction (Andrew Oustimov, University of South Florida, USA)