The translational network for metabolic disease – from protein interaction to disease co-occurrence

Nam, Yonghyun; Lee, Dong-Gi; Bang, Sunjoo; Kim, Ju Han; Kim, Jae-Hoon; Shin, Hyunjung

doi:10.1186/s12859-019-3106-9

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…Differently from previous works on gene/MeSH relations, our statistical framework is independent of the user scope (genes or MeSH can be mined separately) and is not immutable with respect to a species or general topic (e.g. diseases) [48][49][50][51] . Instead, ENQUIRE automatically constructs PubMed queries from network-derived genes and MeSH to expand the input corpus, and in turn the network.…”

Section: Discussionmentioning

confidence: 99%

Enquire Reconstructs and Expands Context-Specific Co-Occurrence Networks From Biomedical Literature

Musella,

Lai,

Widmann

et al. 2023

Preprint

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The accelerating growth in scientific literature is overwhelming our capacity to manually distil complex phenomena like molecular networks linked to diseases. Moreover, confirmation biases in search engines and databases influence the interpretation of facts and the generation of hypotheses. ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers an alternative to manual literature curation and database mining to study complex biomedical phenomena. ENQUIRE generates a co-occurrence network of genes and biomedical ontologies (MeSH) using a corpus of publications as input. The algorithm iteratively reconstructs and expands the network by generating PubMed queries from significant interrelations, until it obtains an interconnected network of genes and MeSH relevant to the input corpus. We systematically evaluated ENQUIRE’s versatility and found that it generates co-occurrence networks similar to those based on co-expression data and manually annotated databases with a fraction of the time and human resources. Using case studies spanning cancer, cell differentiation and immunity, ENQUIRE proved to identify interlinked genes and enriched pathways unique to each topic, thereby preserving their underlying diversity. ENQUIRE supports biomedical researchers by easing literature annotation, boosting hypothesis formulation and facilitating the identification of molecular targets for subsequent experimentation.GRAPHICAL ABSTRACT

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Section: Discussionmentioning

confidence: 99%

Enquire Reconstructs and Expands Context-Specific Co-Occurrence Networks From Biomedical Literature

Musella,

Lai,

Widmann

et al. 2023

Preprint

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“…After constructing the gene network, we examined the effect on DSVPTC through the frequency of mutations and interactions for each group using a machine learning algorithm—the graph-based semi-supervised learning (GSSL) algorithm [ 14 , 15 ]. The GSSL algorithm predicts that nodes with high similarity have similar predictive values for providing the labels of nodes from data expressed in a graphical form (or a network).…”

Section: Methodsmentioning

confidence: 99%

Clinicopathological and Genetic Characteristics of Patients of Different Ages with Diffuse Sclerosing Variant Papillary Thyroid Carcinoma

Kim

Shin

Lee

et al. 2023

Cancers

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Diffuse sclerosing variant papillary thyroid carcinoma (DSVPTC) is commonly observed in young patients, with a median age at diagnosis in the third decade of life. Further, the risk of recurrence is higher for DSVPTC than for classical PTC. Therefore, this study aimed to describe the clinicopathological and genetic characteristics of patients of different ages with DSVPTC. We retrospectively reviewed 397 patients who underwent thyroidectomy for DSVPTC at Gangnam Severance Hospital, Yonsei University, from January 2005 to December 2017. The mean age at diagnosis was 36.7 ± 11.6 years, with most patients (163, 41.1%) aged 31–40 years. DSVPTC was predominant in women (276, 69.5%). We observed recurrence in 46 (11.6%) patients, with regional nodal recurrence being the most common type of recurrence (32 patients, 69.6%). The mean tumour size was larger in younger patients than in older patients. DSVPTC was more aggressive in paediatric patients with a larger-sized tumour, more common multiplicity, and lateral neck metastasis. Through random sampling, we selected 41 patients by age group and examined the mutations in 119 genes using next-generation sequencing. BRAF, KRAS, and TERT displayed relatively higher mutation rates than other genes. DSVPTC displays different clinical, pathological, and molecular profiles than classical PTC. The BRAF, KRAS, and TERT mutations are the most important, with age-specific differences.

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“…Second, we perform comorbidity score prediction: given a specific disease, we predict comorbidity of other diseases by applying graph-based SSL to the constructed network ( Lee et al , 2020 ; Nam et al , 2019a , b ). Since the network includes both synergistic and antagonistic associations (edge weights with positive and negative values), we posited the following hypotheses concerning comorbidity predictions: (i) two diseases have a chance of comorbidity if they are connected (i.e.…”

Section: Methodsmentioning

confidence: 99%

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

et al. 2022

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Motivation Understanding comorbidity is essential for disease prevention, treatment, and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs. Results We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity. Availability The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork Supplementary information Supplementary data are available at Bioinformatics online.

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The translational network for metabolic disease – from protein interaction to disease co-occurrence

Cited by 7 publications

References 49 publications

Enquire Reconstructs and Expands Context-Specific Co-Occurrence Networks From Biomedical Literature

Enquire Reconstructs and Expands Context-Specific Co-Occurrence Networks From Biomedical Literature

Clinicopathological and Genetic Characteristics of Patients of Different Ages with Diffuse Sclerosing Variant Papillary Thyroid Carcinoma

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

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