DEXTER: Disease-Expression Relation Extraction from Text

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen; Hu, Yu; Wu, Cathy H.; Mazumder, Raja; Vijay‐Shanker, K.

doi:10.1093/database/bay045

Cited by 13 publications

(18 citation statements)

References 41 publications

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“…Among 2,280 significantly differentially expressed miRNA-cancer correlations, 208 (9.12%) correlations were recorded in the union of the four databases (HMDD, Mir2disease, miRCancer, and miRiaD) to be exclusively over- or under-expressed in certain types of cancer. Furthermore, we randomly selected 24 of the 90 SDEmiRNAs (with a total of 226 miRNA-cancer interactions) as the input for a text mining tool (DEXTER) [41]. After mapping and manual curation, we extracted 157 (69.47%) interactions (Table S2).…”

Section: Resultsmentioning

confidence: 99%

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Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Dingerdissen

Gupta

et al. 2018

Computers in Biology and Medicine

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microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent nontumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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Section: Resultsmentioning

confidence: 99%

“…We also applied our text mining tool, DEXTER, to extract miRNA-cancer correlations from publications [41]. The basic search term is “ [miRNA name] is [over-expressed / under-expressed / significantly higher / lower] in [cancer tissue/sample] compared to [normal / non-tumorous sample]”.…”

Section: Methodsmentioning

confidence: 99%

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Dingerdissen

Gupta

et al. 2018

Computers in Biology and Medicine

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“…To validate the selected differentially expressed miRNAs, we used four experimentally validated databases generated from literature mining: HMDD (v2.0) [57], Mir2disease (2008) [58], miRCancer (March2016) [59], and miRiaD (2016) [60]. We also applied our literature mining tool, DEXTER, to extract miRNA-cancer correlations from publications [61]. The basic search term is "[miRNA name] is [over-expressed / under-expressed / significantly higher / lower] in [cancer tissue/sample] compared to [normal / non-tumorous sample]".…”

Section: Databases For Validation Of Mirnamentioning

confidence: 99%

Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

Dingerdissen

Gupta

et al. 2018

Preprint

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A number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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“…Therefore, diagnostic systems (Chen et al, 2018) have become more relevant and researchers such as Xia et al (2018) attempt to take on the challenge through the mining of information from sources such as DO, Symptom Ontology (SYMP) and MEDLINE/PubMed citation records. We can also observe in the literature a large volume of studies that use the mining of texts from different unstructured or semi-structured medical information sources (Frunza, Inkpen & Tran, 2011;Mazumder et al, 2016;Singhal, Simmons & Lu, 2016;Xu et al, 2016;Tsumoto et al, 2017;Sudeshna, Bhanumathi & Hamlin, 2017;Aich et al, 2017;Gupta et al, 2018;Rao & Rao, 2018;Zhao et al, 2018); (Bou Rjeily et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

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Background Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system’s API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system’s reliability.

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DEXTER: Disease-Expression Relation Extraction from Text

Cited by 13 publications

References 41 publications

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

DISNET: a framework for extracting phenotypic disease information from public sources

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