Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differential scale-free networks, and we compared our method with existing methods. The comparisons show that our algorithm outperforms these existing methods. For the real datasets, we apply the proposed algorithm on several gene expression datasets and demonstrate that the proposed method provides biologically interesting results.

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is two-fold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.

Biclustering algorithms have been successfully used to find subsets of co-expressed genes under subsets of conditions. In some cases, microarray experiments are performed to compare the biological activities of the genes between two classes of cells, such as normal and cancer cells. In this paper, we propose DiBiCLU S, a novel Differential Biclustering algorithm, to identify differential biclusters from the gene expression data where the samples belong to one of the two classes. The genes in these differential biclusters can be positively or negatively co-expressed. We introduce two criteria for any pair of genes to be considered as a differential pair across the two classes. To illustrate the performance of the proposed algorithm, we present the experimental results of applying DiBiCLU S algorithm on synthetic and reallife datasets. These experiments show that the identified differential biclusters are both statistically and biologically significant.

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Identifying the genes that change between two conditions, such as normal versus cancer, is a crucial task in understanding the causes of diseases. Differential networking has emerged as a powerful approach to achieve this task and to detect the changes in the corresponding network structures. The goal of differential networking is to identify the differentially connected genes between two networks. However, the current differential networking methods primarily depend on pair-wise comparisons of the genes based on their degrees in the two networks. Therefore, these methods cannot capture all the topological changes in the network structure. In this paper, we propose a novel differential networking algorithm, DiffRank, to rank the genes based on their contribution to the differences between two gene co-expression networks. To achieve this goal, we define two novel scoring measures: a local structure measure, differential connectivity, and a global structure measure, differential betweenness centrality. These measures are combined within a PageRank-style framework and optimized by propagating them through the network. Finally, the genes are ranked based on the their propagated scores. We demonstrate the effectiveness of DiffRank on several gene expression datasets, and we show that our method provides biologically interesting rankings.