Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications

Abstract The study is based on the National Science Program 'eHealth in Bulgaria (e-Health)', funded by the Ministry of Education and Science. Partnership Contract No. D-01-200/16.11.2018 Issue Occupational health is closely linked to public health and health system. In Bulgaria there are many software products related to the registration and reporting of occupational health. Description of the Problem It is necessary to study all the determinants of occupational health, including the risks of diseases and accidents in the occupational environment, social and individual factors. The establishment of electronic systems for registering and monitoring both the health status of each worker and the possible hazards in the work environment is associated with the introduction and use of the occupational health record of each worker. Results The methodology for improving the module for occupational diseases in the structure of the occupational health record in Bulgaria has been developed. The classifications are in compliance with the legislation in the country and the requirements of the developing Eurostat methodology for European statistics on occupational diseases are applied. The occupational health record will serve both employers and physicians working in Occupational Health Services. Lessons The occupational disease module in the structure of the occupational health record will contribute to the statistical comparability of occupational disease data at regional and national level. Not only will the registration of the harmful factors of the working environment and the diseases related to the work process, but also the introduction of timely measures to ensure good occupational and public health. Key messages Through the occupational disease module, the structure of the occupational health record introduces the possibility of taking adequate measures to ensure good occupational health. The occupational health record will serve both employers and physicians working in Occupational Health Services.

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Author Correction: Network-based modeling of drug effects on disease module in systemic sclerosis

Scientific Reports ◽

10.1038/s41598-021-87277-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ki-Jo Kim ◽

Su-Jin Moon ◽

Kyung-Su Park ◽

Ilias Tagkopoulos

Keyword(s):

Systemic Sclerosis ◽

Drug Effects ◽

Disease Module

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues

PLoS ONE ◽

10.1371/journal.pone.0010910 ◽

2010 ◽

Vol 5 (6) ◽

pp. e10910 ◽

Cited By ~ 2

Author(s):

Atsushi Niida ◽

Seiya Imoto ◽

Rui Yamaguchi ◽

Masao Nagasaki ◽

Satoru Miyano

Keyword(s):

Gene Expression ◽

Gene Set ◽

Module Discovery

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Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

npj Systems Biology and Applications ◽

10.1038/s41540-020-00168-0 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Paola Paci ◽

Giulia Fiscon ◽

Federica Conte ◽

Rui-Sheng Wang ◽

Lorenzo Farina ◽

...

Keyword(s):

Network Analysis ◽

Interaction Network ◽

Integrated Approach ◽

State Transitions ◽

Disease Genes ◽

Human Interactome ◽

Protein Protein Interaction ◽

Interactome Network ◽

Module Discovery ◽

Specific Disorders

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

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Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

10.1101/786798 ◽

2019 ◽

Author(s):

Hongzhu Cui ◽

Suhas Srinivasan ◽

Dmitry Korkin

Keyword(s):

Biological Networks ◽

Molecular Mechanisms ◽

Association Studies ◽

Genetic Diseases ◽

Detection Methods ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Human Interactome ◽

Module Detection ◽

Disease Module

AbstractProgress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.

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Network Analysis of Inflammatory Bowel Disease Reveals PTPN2 As New Monogenic Cause of Intestinal Inflammation

10.1101/768028 ◽

2019 ◽

Author(s):

Marianna Parlato ◽

Julia Pazmandi ◽

Qing Nian ◽

Fabienne Charbit-Henrion ◽

Bernadette Bègue ◽

...

Keyword(s):

Inflammatory Bowel Disease ◽

Random Walk ◽

Bowel Disease ◽

Fine Tuning ◽

Molecular Networks ◽

Genome Wide Association Studies ◽

Causal Genes ◽

Disease Module ◽

Inflammatory Bowel ◽

Stat Pathway

ABSTRACTBACKGROUND & AIMSGenome-wide association studies (GWAS) have uncovered multiple loci associated with inflammatory bowel disease (IBD), yet delineating functional consequences is complex. We used a network-based approach to uncover traits common to monogenic and polygenic forms of IBD in order to reconstruct disease relevant pathways and prioritize causal genes.METHODSWe have used an iterative random walk with restart to explore network neighborhood around the core monogenic IBD cluster and disease-module cohesion to identify functionally relevant GWAS genes. Whole exome sequencing was used to screen a cohort of monogenic IBD for germline mutations in top GWAS genes. One mutation was identified and validated by a combination of biochemical approaches.RESULTSMonogenic IBD genes clustered siginificantly on the molecular networks and had central roles in network topology. Iterative random walk from these genes allowed to rank the GWAS genes, among which 14 had high disease-module cohesion and were selected as putative causal genes. As a proof of concept, a germline loss of function mutation was identified in PTPN2, one of the top candidates, as a novel genetic etiology of early-onset intestinal autoimmunity. The mutation abolished the catalytic activity of the enzyme, resulting in haploinsufficiency and hyper-activation of the JAK/STAT pathway in lymphocytes.CONCLUSIONSOur network-based approach bridges the gap between large-scale network medicine prediction and single-gene defects and underscores the crucial need of fine tuning the JAK/STAT pathway to preserve intestinal immune homeostasis. Our data provide genetic-based rationale for using drugs targeting the JAK/STAT pathway in IBD.

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The effect of statistical normalisation on network propagation scores

Bioinformatics ◽

10.1093/bioinformatics/btaa896 ◽

2020 ◽

Author(s):

Sergio Picart-Armada ◽

Wesley K Thompson ◽

Alfonso Buil ◽

Alexandre Perera-Lluna

Keyword(s):

Protein Function ◽

Diffusion Processes ◽

Protein Function Prediction ◽

Interaction Network ◽

Mean Value ◽

Statistical Properties ◽

Label Propagation ◽

Supplementary Information ◽

Module Discovery ◽

Permutation Analysis

Abstract Motivation Network diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels. Results Diffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities. Availability The code is publicly available at https://github.com/b2slab/diffuBench Supplementary information Supplementary data are available at Bioinformatics online.

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