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This paper provides a novel characterization of nested complexes in protein interaction networks, stressing definition and representation issues, quantification, biological validation, network metrics, motifs, modularity and gene ontology (GO) terms. This characterization can be used in the design of a nested protein complex prediction algorithm. We introduce the "nested group" concept as a way to represent nested complexes. We also show that enrichment in essential proteins, GO terms related to regulation, imperfect 5-clique motifs, as well as higher GO homogeneity, can be used to identify proteins in nested complexes. Supplementary materials, data and programs are available at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Summarization: Statistical evaluation of temporal gene expression profiles plays an important role in particular biological processes and conditions. We introduce a clustering method for this purpose, which is based on the expression patterns but is also influenced by temporal changes. We compare the results of our platform with methods based on expression or the rank of temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of primary human chondrocytes and mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes, which can support biological performance. For statistical evaluation we introduce a validity measure that takes under consideration these temporal changes and we also perform an enrichment analysis of three central genes in each cluster. Even though we can detect certain statistical similarities, these might be due to different biological processes. Our proposed platform contributes to both the statistical and biological validation of temporal profiles. Παρουσιάστηκε στο: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.

Mining published articles in biology and medicine is a favored means of identifying potential biomarkers in comparison to conventional reviewing process. This is made possible by the development of public literature databases and data mining algorithms. In this article, we present a method to extract novel protein interactions from online full-text articles for biomarker discovery. By evaluating support and confidence metrics, explicit and implicit protein interactions are extracted from corpus of articles. By properly chosen minimum support and confidence, our method maximizes the identification of known interactions while minimizing the number of novel interactions. Hence, our method provides a manageable size of novel interactions for biological validation.