Systems Biology Approaches to Mining High Throughput Biological Data

With advances in high throughput measurement techniques, large-scale biological data have been and will continuously be produced, for example, gene expression data, protein-protein interaction (PPI) data, tandem mass spectra data, microRNA expression data, lncRNA expression data, and biomolecule-disease association data. Such data contain insightful information for understanding the mechanism of molecular biological systems and have proved useful in diagnosis, treatment, and drug design for genetic disorders or complex diseases. For this focus issue, we have invited the researchers to contribute original research articles which develop or improve systems biology approaches to mining high throughput biological data. With high throughput data, it is appealing to develop systems biology approaches to understand important biological processes. In the paper " Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization, " Y. Dou et al. developed a new tool for the identification of differentially expressed genes with RNA-Seq data, named GExposer. This tool introduced a local normalization algorithm to reduce the bias of nonran-domly positioned read depth. The Naive Bayes classifier was employed to integrate fold change, transcript length, and GC-content to identify differentially expressed genes. Results on several independent tests showed that GExposer had better performance than other methods. In the paper " K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data, " K. Wang et al. designed the nonlinear K-profiles clustering method, which can be seen as the nonlinear counterpart of the K-means clustering algorithm. The method had a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles. Results from extensive simulation studies showed that K-profiles clustering outperformed traditional linear K-means algorithm. In addition, K-profile clustering generated biologically meaningful results from a gene expression dataset. Replicative senescence is of fundamental importance for the process of cellular aging. In the paper " Similarities in Gene Expression Profiles during In Vitro Aging of Primary Human Embryonic Lung and Foreskin Fibroblasts, " S. Diek-mann et al. elucidated cellular aging process by comparing gene expression changes, measured by RNA-Seq, in fibrob-lasts originating from two different tissues, embryonic lung (MRC-5) and foreskin (HFF), at five different time points during their transition into senescence. Their results showed that a number of monotonically up-and downregulated genes had a novel strong functional link to aging and senescence related processes. More and more studies have shown that many complex diseases are contributed jointly …