DiMeX: A Text Mining System for Mutation-Disease Association Extraction

Mahmood, A. S. M. Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay‐Shanker, K.

doi:10.1371/journal.pone.0152725

Cited by 52 publications

(40 citation statements)

References 42 publications

(51 reference statements)

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“…Several efforts addressing information extraction and text classification tasks benefit from an integrated approach. For instance, Botsis et al, developed a common decision support environment for medical product safety surveillance [73], while others used NLP to build databases of clinically useful information, such as mutation-disease associations [74], drug side-effects by parsing product labels [75], and genetic alteration information in cancer trials [76]. This includes work on annotated text corpora, e.g.…”

Section: Text Classification and Information Extraction Remain Strongmentioning

confidence: 99%

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Névéol¹,

Zweigenbaum²

2017

Yearb Med Inform

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SummaryObjectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives. Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.

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Section: Text Classification and Information Extraction Remain Strongmentioning

confidence: 99%

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Névéol¹,

Zweigenbaum²

2017

Yearb Med Inform

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“…Therefore, diagnostic systems (Chen et al, 2018) have become more relevant and researchers such as Xia et al attempt to take on the challenge through the mining of information from sources such as DO, Symptom Ontology (SYMP) and MEDLINE/PubMed citation records (Xia et al, 2018). 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%

Peer Review #1 of "DISNET: a framework for extracting phenotypic disease information from public sources (v0.1)"

2020

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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, 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 (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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“…The large amount of data generated from these studies [4] necessitates the need to develop an automatic approach in order to facilitate the study of the extracted associations. Recently, a few corpora and methods have been developed with the aim of extracting mutation and disease associations from texts such as [5] and [6]. There is, on the other hand, no available corpus for extracting the association of SNP-phenotypes from texts annotated with negation, modality, and the confidence degree of such associations.…”

Section: Introductionmentioning

confidence: 99%

“…PKDE4J [5] and Dimex [6] are two methods for extracting mutation and disease association, the latter being a rule-based unsupervised mutation-disease association extraction working on the abstract level. The PKDE4J, however, is a supervised method that employs a rich set of rules to detect the used features.…”

Section: Introductionmentioning

confidence: 99%

SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature

et al. 2017

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BackgroundSingle Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpose of extracting mutations and diseases from texts. However, there is no available corpus, for extracting associations from texts, that is annotated with linguistic-based negation, modality markers, neutral candidates, and confidence level of associations.MethodIn this research, different steps were presented so as to produce the SNPPhenA corpus. They include automatic Named Entity Recognition (NER) followed by the manual annotation of SNP and phenotype names, annotation of the SNP-phenotype associations and their level of confidence, as well as modality markers. Moreover, the produced corpus was annotated with negation scopes and cues as well as neutral candidates that play crucial role as far as negation and the modality phenomenon in relation to extraction tasks.ResultThe agreement between annotators was measured by Cohen’s Kappa coefficient where the resulting scores indicated the reliability of the corpus. The Kappa score was 0.79 for annotating the associations and 0.80 for the confidence degree of associations. Further presented were the basic statistics of the annotated features of the corpus in addition to the results of our first experiments related to the extraction of ranked SNP-Phenotype associations. The prepared guideline documents render the corpus more convenient and facile to use. The corpus, guidelines and inter-annotator agreement analysis are available on the website of the corpus: http://nil.fdi.ucm.es/?q=node/639.ConclusionSpecifying the confidence degree of SNP-phenotype associations from articles helps identify the strength of associations that could in turn assist genomics scientists in determining phenotypic plasticity and the importance of environmental factors. What is more, our first experiments with the corpus show that linguistic-based confidence alongside other non-linguistic features can be utilized in order to estimate the strength of the observed SNP-phenotype associations. Trial Registration: Not ApplicableElectronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0116-2) contains supplementary material, which is available to authorized users.

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DiMeX: A Text Mining System for Mutation-Disease Association Extraction

Cited by 52 publications

References 42 publications

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

Peer Review #1 of "DISNET: a framework for extracting phenotypic disease information from public sources (v0.1)"

SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature

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