Systematic identification of latent disease-gene associations from PubMed articles

Zhang, Yuji; Shen, Feichen; Rastegar-Mojarad, Majid; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

doi:10.1371/journal.pone.0191568

Cited by 19 publications

(13 citation statements)

References 60 publications

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“…Results showed that the HPO-Orphanet+ is capable of providing a diagnostic graph mixed with both rare and common diseases, which has potential usage in rare disease differential diagnosis, especially for those rare diseases sharing similar symptoms with common diseases. In the future, we will upgrade the HPO-Orphanet+ by mining disease-gene information from literature [11, 63]. In addition, one recent research proposed a novel idea by introducing the concept of “property” as a third layer in addition to traditional two-layer disease-phenotype relationship [64].…”

Section: Discussionmentioning

confidence: 99%

“…Some other existing studies investigated the mining of associations between diseases and genes. For example, Zhang et al combined the Latent Dirichlet Allocation (LDA) [9] with network-based computational approach [10] to discover disease-gene associations from large amount of PubMed literature [11]. Piro et al developed a classification approach to predict disease-gene associations [12].…”

Section: Introductionmentioning

confidence: 99%

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Rare disease knowledge enrichment through a data-driven approach

Shen

Zhao

Wang

et al. 2019

BMC Med Inform Decis Mak

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Background Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). Methods We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs. Results We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer. Conclusions Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis. Electronic supplementary material The online version of this article (10.1186/s12911-019-0752-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rare disease knowledge enrichment through a data-driven approach

Shen

Zhao

Wang

et al. 2019

BMC Med Inform Decis Mak

Self Cite

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“…In the future, we will combine graph network analysis approaches [58] [59] with clustering algorithm to provide network motif [60] analysis and LDA-based topic modelling [61]. Furthermore, parallel and distributed algorithms, using the indexing technique, will be developed.…”

Section: Discussionmentioning

confidence: 99%

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data

Shen¹,

Lee²

2018

JILSA

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A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.

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“…Investigation of disease-associated genes can improve the understanding of disease etiology and development, thereby facilitating design and development of novel preventive and treatment strategies (7,8). Cross disease-gene studies and further pathway analyses provide an opportunity to resolve overlapping associations into discrete pathways and investigate possible shared etiologies (9,10).…”

Section: Introductionmentioning

confidence: 99%

Cross‑disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

Zhang¹,

Wang²,

Huang³

et al. 2019

Oncol Lett

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Lung squamous cell carcinoma (LSCC) exhibits a number of similarities with lung adenocarcinoma (LA) in terms of copy number alterations. However, compared with LA, the range of genetic alterations in LSCC is less understood. In the present study, a large-scale literature-based search of LA-associated genes and LSCC-associated genes was performed to identify the genetic basis in common with these two diseases. For each of the LA-associated genes, a mega-analysis was performed to test its expression variations in LSCC using 11 RNA expression datasets, with significant genes identified using statistical analysis. Subsequently, a functional pathway analysis was performed to identify a possible association between any of the significant genes identified from the mega-analysis and LSCC, followed by a co-expression analysis. A multiple linear regression (MLR) model was employed to investigate the possible influence of sample size, country of origin and study date on gene expression in patients with LSCC. Disease-gene association data analysis identified 1,178 genes involved in LA, 334 in LSCC, with a significant overlap of 187 genes (P<1.02× −161 ). Mega-analysis revealed that three LA-associated genes, such as solute carrier family 2 member 1 (SLC2A1), endothelial PAS domain protein 1 (EPAS1) and cyclin-dependent kinase 4 (CDK4), were significantly associated with LSCC (P<1.60×10 −8 ), with multiple potential pathways identified by functional pathway analysis, which were further validated by co-expression analysis. The present MLR analysis suggested that the country of origin was a significant factor for the levels of expression of all three genes in patients with LSCC (P<4.0×10 −3 ). Collectively, the present results suggested that genes associated with LA should be further investigated for their association with LSCC. In addition, SLC2A1, EPAS1 and CDK4 may be novel risk genes associated with LA and LSCC.

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Systematic identification of latent disease-gene associations from PubMed articles

Cited by 19 publications

References 60 publications

Rare disease knowledge enrichment through a data-driven approach

Rare disease knowledge enrichment through a data-driven approach

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data

Cross‑disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

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