The current advances in proteomic and transcriptomic technologies produced huge amounts of high-throughput data that spans multiple biological processes and characteristics in different organisms.One of the important directions in today's bioinformatics research is to discover patterns of genes that have interesting properties.These groups of genes can be referred to as functional modules.Detecting functional modules can be accomplished by the deep analysis of protein-protein interaction (PPI) networks, gene expression profiles, or both.In this work the focus will be on Human protein-protein interaction network and genes expression data that represents genes behavior in a group of diseases.Two of the most well-established clustering methods that target the interaction networks and the expression data will be used in this analysis.In addition, and to have more insights, genes molecular functionality will be studied.Finally, I will introduce the relation of the extracted modules on biological pathways.This study mainly illustrates the importance of including protein interaction activities as part of any study that aims at discovering meaningful knowledge about the biological scene where many actors play different roles.

Insect diapause (dormancy) synchronizes an insect's life cycle to seasonal changes in the abiotic and biotic resources required for development and reproduction. Transcription analysis of diapause to post-diapause quiescent transition in the alfalfa leafcutting bee Megachile rotundataFabricius identifies 643 post-diapause up-regulated gene transcripts and 242 post-diapause down-regulated transcripts. The log(2) fold change in gene expression levels ranges from -5 to 7. Transcripts from several pivotal diapause-related processes, including chromatin remodelling, cellular signalling pathways, microRNA processing, anaerobic glycolysis, cell cycle arrest and neuroendocrine control, are identified as being differentially expressed during the diapause to post-diapause transition. In conjunction with studies from other insect species, the data indicate that there are several common mechanisms of diapause control and maintenance.

With the abundance of vast amounts of "-omic" biological data from multiple sources that span diverse biological processes, approaches for answering critical questions become a vital mission. To grasp more comprehensive view of the biology of the organism, recent research has focused on integrating multiple sources of data. This integration provides more insights and helps reduce the impact of problems each source may have. Recent research showed that differentially expressed, or dysregulated, patterns of interacting proteins exhibit more interesting properties with respect to many complex phenotypes. In this work we follow an integrative approach by combining the physical protein-protein interaction, PPI, network with gene expression data for a number of diseases and phenotypes. In this study, we propose an algorithm for mining Dysregulated Phenotype-Related interacting genes, DPRs. Experimental results on 88 Human gene expression datasets that were annotated by employing UMLS mapping demonstrate the effectiveness of the algorithm in discovering biologically and statistically significant DPRs.

Recent advances in proteomic and transcriptomic technologies resulted in the accumulation of vast amount of high-throughput data that span multiple biological processes and characteristics in different organisms. Much of the data come in the form of interaction networks and mRNA expression arrays. An important task in systems biology is functional modules discovery where the goal is to uncover well-connected sub-networks (modules). These discovered modules help to unravel the underlying mechanisms of the observed biological processes. While most of the existing module discovery methods use only the interaction data, in this work we propose, CLARM, which discovers biological modules by incorporating gene profiles data with protein-protein interaction networks. We demonstrate the effectiveness of CLARM on Yeast and Human interaction datasets, and gene expression and molecular function profiles. Experiments on these real datasets show that the CLARM approach is competitive to well established functional module discovery methods.

With the availability of vast amounts of protein-protein, protein-DNA interactions, and genome-wide mRNA expression data for several organisms, identifying biological complexes has emerged as a major task in systems biology. Most of the existing approaches for complex identification have focused on utilizing one source of data. Recent research has shown that systematic integration of gene profile data with interaction data yields significant patterns. In this paper, we introduce the problem of mining maximal cohesive subnetworks that satisfy user-defined constraints defined over the gene profiles of the reported subnetworks. Moreover, we introduce the problem of finding maximal cohesive patterns which are sets of cohesive genes. Experiments on Yeast and Human datasets show the effectiveness of the proposed approach by assessing the overlap of the discovered subnetworks with known biological complexes. Moreover, GO enrichment analysis shows that the discovered subnetworks are biologically significant.

With the availability of vast amounts of protein-protein, protein-DNA interactions, and genome-wide mRNA expression data for several organisms, identifying biological complexes has emerged as a major task in systems biology. Most of the existing approaches for complex identification have focused on utilizing one source of data. Recent research has shown that systematic integration of gene profile data with interaction data yields significant patterns. In this paper, we introduce the problem of mining maximal cohesive subnetworks that satisfy user-defined constraints defined over the gene profiles of the reported subnetworks. Moreover, we introduce the problem of finding maximal cohesive patterns which are sets of coehsive genes. Experiments on Yeast and Human datasets show the effectiveness of the proposed approach by assessing the overlap of the discovered subnetworks with known biological complexes. Moreover, GO enrichment analysis show that the discovered subnetworks are biologically significant. The proposed algorithm takes only seconds to several minutes to run on the Human dataset depending on how stringent the user-defined constraint is.

Functional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM , a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to well-established module discovery methods.

Many online social networks such as Face book, Linked In and My Space have become increasingly important. These social networks are rich in information about entities like hobbies, demographic information, friendship, and other attributes. This information can be used extensively for network analysis. One of the most important problems in social network analysis is community detection. The community detection problem is closely related to graph clustering. Most of the existing graph clustering algorithms employ only the structure of a graph to find highly connected components. These algorithms ignore nodes' attributes that can help in improving the quality of the clustering. In this paper, we propose a clustering algorithm which clusters a graph by incorporating both the topological structure of the graph as well as attribute information. The aim is to find clusters such that the nodes in each cluster are similar in the attribute space. In terms of social networks, we are looking to find communities where the members of the same community have similar profiles. The method was evaluated using real and synthetic graph datasets. The experimental results demonstrate the effectiveness of the proposed method.