Signed Graphlets based Gene Expression Analysis Since the introduction of microarray technology, collecting and analyzing gene expression data to identify biomarkers for clinical applications such as diagnosis, prognosis, andor prediction of suitable treatment has been widely practiced. Yet the progress is slow and limited. In this paper, we propose a new and relatively simple technique based on signed graphlets to reduce gene expression data of any large size to a constant size heatmap. The heatmaps obtained from gene expression data collected under various conditions, we believe, could be used to derive biomarkers or could be used themselves as biomarkers of different cellular states. A database of such biomarker signatures could increase the accuracy and efficacy of the clinical applications of diagnosis, prognosis, and hence choosing suitable treatment options. We demonstrate our approach by analyzing gene expression data obtained from three types of cell samples  one from healthy cells and two from two types of cancer cells. The initial results are encouraging. As a test of robustness, we repeated the computation with 6 sets of random samples from each type and constructed the heatmaps. We observe that, within each sample type, the heatmaps are noticeably invariant.

MultiClassification of Cancer Samples Based on CoExpression Analyses Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multiclassification of differential geneexpressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multiclassification technique, based on coexpression patterns specific to individual subclasses in provided training data as coexpression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive coexpression patterns in individual samples, which is solved through comparing coexpression patterns within a subclass and those in the subclass plus a new sample. Compared with the stateoftheart gene expressionbased classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the coexpressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.