Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks.

BACKGROUND: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. METHODS: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. RESULTS: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Direct and Indirect Determinants of Body Mass Index in Both Major Ethnic Groups Experiencing the Nutritional Transition in Cameroon.

In the context of rapid nutritional transitions in Africa, few studies have analyzed the etiology of obesity by considering the driver pathways that predict body mass index (BMI). The aim of this study is to innovatively identify these driver pathways, including the main sociodemographic and socioecological drivers of BMI. We conducted a rural-urban quantitative study in Cameroon (n = 1106; balanced sex ratio) to explore this issue. We recruited participants and reported several sociodemographic characteristics (e.g., marital status, socioeconomic status (SES), and ethnicity). We then assessed three main socioecological drivers of BMI (body weight perception, dietary intake, and physical activity) and conducted bioanthropometric measurements. We identified several driver pathways predicting BMI. In Cameroon, Bamiléké ethnicity, higher SES, being married, and older age had positive effects on BMI through overweight valorization and/or dietary intake. Accordingly, we found that being Bamiléké, married, and middle-aged, as well as having a higher SES, were factors that constituted at-risk subgroups overexposed to drivers of obesity. As such, this study highlights the necessity of investigating the complex driver pathways that lead to obesity. Therefore, better identification of the subgroups at risk for obesity will help in developing more targeted population health policies in countries where this burden is a major public health issue.

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context.