ArticlePloS one2011
Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 89 citations in OpenAlex.
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- Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy.Entropy (Basel, Switzerland) · 2022Article
- A QUBO formulation for top-τ eigencentrality nodes.PloS one · 2022Article
- Determination of biomarkers from microarray data using graph neural network and spectral clustering.Scientific reports · 2021Article
- Finding associations in a heterogeneous setting: statistical test for aberration enrichment.Genome medicine · 2021Article
- Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases.In silico biology · 2021Article
- Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis.Network neuroscience (Cambridge, Mass.) · 2021Article
- Unveiling new disease, pathway, and gene associations via multi-scale neural network.PloS one · 2020Article
- A network-based analysis for mining the risk pathways in glioblastoma.Oncology letters · 2019Article
- Prediction of seed gene function in progressive diabetic neuropathy by a network-based inference method.Experimental and therapeutic medicine · 2019Article
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- Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach.Molecular medicine reports · 2018Article
- Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.BMC systems biology · 2017Article
- Identifying key genes in glaucoma based on a benchmarked dataset and the gene regulatory network.Experimental and therapeutic medicine · 2017Article
- HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions.BMC genomics · 2017Article
- The integration of weighted human gene association networks based on link prediction.BMC systems biology · 2017Article
- An integrative approach to investigate the respective roles of single-nucleotide variants and copy-number variants in Attention-Deficit/Hyperactivity Disorder.Scientific reports · 2016Article
- Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity.Genome research · 2015Article
- Identification of active miRNA and transcription factor regulatory pathways in human obesity-related inflammation.BMC bioinformatics · 2015Article
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Authors and funding
4 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.