Network diffusion with centrality measures to identify disease-related genes

Janyasupab, Panisa; Suratanee, Apichat; Plaimas, Kitiporn

doi:10.3934/mbe.2021147

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…With the use of network analysis, central node identification using various centrality measurements and community detection by several network clustering algorithms [ 46 , 47 ] have been widely used in much research. These approaches were successfully applied in several applications to identify key disease-related genes, disease–disease associations, disease–protein associations, and drug–disease associations [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Additionally, the benefit of the network analysis is drug repositioning or drug repurposing, characterized by discovering a new role of treatment from existing drugs based on the key disease-related genes identified from the biological network [ 58 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

Sagulkoo

Chuntakaruk

Rungrotmongkol

et al. 2022

JPM

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The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

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Section: Introductionmentioning

confidence: 99%

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

Sagulkoo

Chuntakaruk

Rungrotmongkol

et al. 2022

JPM

Self Cite

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“…The NeDBIT features include two network diffusion-based features, namely heat diffusion and balanced diffusion, and two biology-informed topological metrics, namely NetShort and NetRing. Network diffusion methods are widely used in disease gene discovery processes ( Janyasupab et al , 2021 ; Lancour et al , 2018 ; Picart-Armada et al , 2019 ). We coupled network diffusion methods and innovative topological-based features in order to make the most of the combined predictive power of both approaches.…”

Section: Methodsmentioning

confidence: 99%

NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification

et al. 2023

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Motivation Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning setting in which only a subset of instances are labeled as positive while the rest of the data set is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. Results The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on ten different disease data sets using three machine learning algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms. Availability and implementation The source code of NIAPU can be accessed at https://github.com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites. Supplementary information Supplementary data are available at Bioinformatics online.

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“…. , 100}, we then used K-NN classification to estimate the posterior probabilities as follows ([•] is the Iverson bracket, i. e., [true] = 1 and [false] = 0): (10)…”

Section: Design Of Simulation Studymentioning

confidence: 99%

Emergence of power-law distributions in protein-protein interaction networks through study bias

Lucchetta

List

Blumenthal

et al. 2023

Preprint

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Protein-protein interaction (PPI) networks have been found to be power-law-distributed, ie, in observed PPI networks, the fraction of nodes with degree k often follows a power-law (PL) distribution k-α. The emergence of this property is typically explained by evolutionary or functional considerations. However, the experimental procedures used to detect PPIs are known to be heavily affected by technical and study bias. For instance, proteins known to be involved in cancer are often heavily overstudied and proteins used as baits in large-scale experiments tend to have many false-positive interaction partners. This raises the question whether PL distributions in observed PPI networks could be explained by these biases alone. Here, we address this question using statistical analyses of the degree distributions of 1000s of observed PPI networks of controlled provenance as well as simulation studies. Our results indicate that study bias and technical bias can indeed largely explain the fact that observed PPI networks tend to be PL-distributed. This implies that it is problematic to derive hypotheses about the degree distribution and emergence of the true biological interactome from the PL distributions in observed PPI networks.

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Network diffusion with centrality measures to identify disease-related genes

Cited by 15 publications

References 40 publications

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification

Emergence of power-law distributions in protein-protein interaction networks through study bias

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