Identifying functional modules in protein–protein interaction networks: an integrated exact approach

Dittrich, Marcus; Klau, Gunnar W.; Rosenwald, Andreas; Dandekar, Thomas; Müller, Tobias

doi:10.1093/bioinformatics/btn161

Cited by 468 publications

(534 citation statements)

References 33 publications

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“…The algorithm applies a rigorous statistical measure for scoring network modules by calibrating the z-score against the background distribution. Other algorithms for identifying active modules are Heinz (Dittrich et al 2008), CEZANNE (Ulitsky & Shamir 2009), and CASNet (Gaire et al 2013). The algorithms HotNet (Vandin et al 2011;Leiserson et al 2015) and HyperModules (Leung et al 2014) identify statistically mutated subnetworks among patients using local network search heuristics to detect closely connected network regions.…”

Section: Disease Gene Prioritization By Network Propagationmentioning

confidence: 99%

Network-assisted approaches for human disease research

Shim

Lee

2015

Animal Cells and Systems

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Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and disease pathways is crucial. The functional interdependence between genes for disease progression has been identified by their connections in gene networks, which enables prediction of novel disease genes based on their connections to known disease genes. Disease modules can be identified by subnetworks that are enriched for patientspecific activated or mutated genes. Network biology also facilitates the subtyping of complex diseases such as cancer, which is a prerequisite for developing personalized medicinal therapies. In this review, we discuss network-assisted approaches in human disease research, with particular focus on the three major tasks. Network biology will provide powerful research platforms to dissect and interpret disease genomics data in the future.

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Section: Disease Gene Prioritization By Network Propagationmentioning

confidence: 99%

Network-assisted approaches for human disease research

Shim

Lee

2015

Animal Cells and Systems

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“…We use the light-weight Heinz library provided by the authors of [8], which solves a Max-Weighted-Connected-Subgraph (MWCS) problem. Given a PCST problem over graph G ,we first convert it to a MWCS problem over an augmented graph Z, and then solve that using the Heinz library.…”

Section: Definition 2: (Prize-collecting-steiner-tree (Pcst) Problem)mentioning

confidence: 99%

Part Bricolage: Flow-Assisted Part-Based Graphs for Detecting Activities in Videos

Shankar

Badrinarayanan

Cipolla

2014

Computer Vision – ECCV 2014

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Abstract. Space-time detection of human activities in videos can significantly enhance visual search. To handle such tasks, while solely using low-level features has been found somewhat insufficient for complex datasets; mid-level features (like body parts) that are normally considered, are not robustly accounted for their inaccuracy. Moreover, the activity detection mechanisms do not constructively utilize the importance and trustworthiness of the features.This paper addresses these problems and introduces a unified formulation for robustly detecting activities in videos. Our first contribution is the formulation of the detection task as an undirected node-and edge-weighted graphical structure called Part Bricolage (PB), where the node weights represent the type of features along with their importance, and edge weights incorporate the probability of the features belonging to a known activity class, while also accounting for the trustworthiness of the features connecting the edge. Prize-Collecting-SteinerTree (PCST) problem [19] is solved for such a graph that gives the best connected subgraph comprising the activity of interest. Our second contribution is a novel technique for robust body part estimation, which uses two types of state-of-the-art pose detectors, and resolves the plausible detection ambiguities with pre-trained classifiers that predict the trustworthiness of the pose detectors. Our third contribution is the proposal of fusing the low-level descriptors with the mid-level ones, while maintaining the spatial structure between the features.For a quantitative evaluation of the detection power of PB, we run PB on Hollywood and MSR-Actions datasets and outperform the state-of-the-art by a significant margin for various detection paradigms.

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“…Despite being incomplete (26) and error-prone (27), systematic studies of protein interaction networks have been proven to be particularly important for deciphering the relationships between network structure and function (28), discovering novel protein function (29), identifying functionally coherent modules (30,31), and conserved molecular interaction patterns (32,33). In addition, interaction networks have become essential and powerful tools for associating proteins with distinct phenotypes and diseases (34,35), as well as for studying pharmacological drug-target relationships (36,37).…”

Section: Protein Interactions and Networkmentioning

confidence: 99%

From protein interaction networks to novel therapeutic strategies

Jaeger

Aloy

2012

IUBMB Life

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SummaryCellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.2012 IUBMB IUBMB Life, 64(6): 529-537, 2012

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Identifying functional modules in protein–protein interaction networks: an integrated exact approach

Cited by 468 publications

References 33 publications

Network-assisted approaches for human disease research

Network-assisted approaches for human disease research

Part Bricolage: Flow-Assisted Part-Based Graphs for Detecting Activities in Videos

From protein interaction networks to novel therapeutic strategies

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