Compared to the available protein sequences of different organisms, the number of revealed protein–protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

To pursue a systematic approach to discovery of mechanisms of action of traditional Chinese medicine (TCM), we used microarrays, bioinformatics and the “Connectivity Map” (CMAP) to examine TCM-induced changes in gene expression. We demonstrated that this approach can be used to elucidate new molecular targets using a model TCM herbal formula Si-Wu-Tang (SWT) which is widely used for women's health. The human breast cancer MCF-7 cells treated with 0.1 µM estradiol or 2.56 mg/ml of SWT showed dramatic gene expression changes, while no significant change was detected for ferulic acid, a known bioactive compound of SWT. Pathway analysis using differentially expressed genes related to the treatment effect identified that expression of genes in the nuclear factor erythroid 2-related factor 2 (Nrf2) cytoprotective pathway was most significantly affected by SWT, but not by estradiol or ferulic acid. The Nrf2-regulated genes HMOX1, GCLC, GCLM, SLC7A11 and NQO1 were upreguated by SWT in a dose-dependent manner, which was validated by real-time RT-PCR. Consistently, treatment with SWT and its four herbal ingredients resulted in an increased antioxidant response element (ARE)-luciferase reporter activity in MCF-7 and HEK293 cells. Furthermore, the gene expression profile of differentially expressed genes related to SWT treatment was used to compare with those of 1,309 compounds in the CMAP database. The CMAP profiles of estradiol-treated MCF-7 cells showed an excellent match with SWT treatment, consistent with SWT's widely claimed use for women's diseases and indicating a phytoestrogenic effect. The CMAP profiles of chemopreventive agents withaferin A and resveratrol also showed high similarity to the profiles of SWT. This study identified SWT as an Nrf2 activator and phytoestrogen, suggesting its use as a nontoxic chemopreventive agent, and demonstrated the feasibility of combining microarray gene expression profiling with CMAP mining to discover mechanisms of actions and to identify new health benefits of TCMs.