An analysis of disease-gene relationship from Medline abstracts by DigSee

Kim, Jeong-Kyun; Kim, Jung‐Jae; Lee, Hyunju

doi:10.1038/srep40154

Cited by 46 publications

(48 citation statements)

References 23 publications

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“…Multiple previous methods for text mining gene, disease, variant, and phenotype information from literature have been developed (42)(43)(44)(45)(46)(47)(48)(49)(50)(51), most of which curate limited information or information from comparatively small sets of articles. Of 3 additional efforts (52)(53)(54) attempting to curate gene-phenotype information from a broad set of articles in automatic or semi-automatic fashion, we focused on DisGeNET (the most recently updated such database), containing genephenotype relationships, disease-causing variants, and links to primary literature from PubMed.…”

Section: Amelie Knowledgebase Outperforms Most Up-to-date Automaticalmentioning

confidence: 99%

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

Birgmeier

Haeussler

Deisseroth

et al. 2019

Preprint

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The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication/s supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts, downloads and further parses hundreds of thousands of full text articles in search of information supporting the causality and associated phenotypes of any published genetic variant. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient's given set of phenotypes. Diagnosis of singleton patients (without relatives' exomes) is the most time-consuming scenario. AMELIE ranked the causative gene at the very top in 2/3 of 215 diagnosed singleton Mendelian patients. Evaluating only the top 11 AMELIE scored genes of 127 (median) candidate genes per patient results in rapid diagnosis for 90+% of cases. AMELIE-based evaluation of all cases is 3-19x more efficient than hand-curated database-based approaches. We replicate these results on a cohort of clinical cases from Stanford Children's Health and the Manton Center for Orphan DiseaseResearch. An analysis web portal with our most recent update, programmatic interface and code will be available at AMELIE.stanford.edu. A pilot run of the web portal has already served many thousands of job submissions from dozens of countries.

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Section: Amelie Knowledgebase Outperforms Most Up-to-date Automaticalmentioning

confidence: 99%

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

Birgmeier

Haeussler

Deisseroth

et al. 2019

Preprint

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“…Another critical challenge for the extraction of associations is the scarcity of easy-to-use systems for curators, and the lack of helpful outputs to be utilized by them to feed databases. Altogether, this has led to a scarce representation of several genes and diseases in state-of-the-art databases, as shown by others Kim et al, 2017), further complicating their study by researchers of different fields. In this work, we tried to tackle this problem by developing a system that uses as input only a list of PubMed IDs, and returns associations in a curation-oriented format.…”

Section: Introductionmentioning

confidence: 99%

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

Lm¹,

Olayo-Alarcón²,

et al. 2019

Preprint

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Motivation:The genetic mechanisms involved in human diseases are fundamental in biomedical research. Several databases with curated associations between genes and diseases have emerged in the last decades. Although, due to the demanding and time consuming nature of manual curation of literature, they still lack large amounts of information. Current automatic approaches extract associations by considering each abstract or sentence independently. This approach could potentially lead to contradictions between individual cases. Therefore, there is a current need for automatic strategies that can provide a literature consensus of gene-disease associations, and are not prone to making contradictory predictions.Results: Here, we present GeDex, an effective and freely available automatic approach to extract consensus gene-disease associations from biomedical literature based on a predictive model trained with four simple features. As far as we know, it is the only system that reports a single consensus prediction from multiple sentences supporting the same association. We tested our approach on the curated fraction of DisGeNet (f-score 0.77) and validated it on a manually curated dataset, obtaining a of 1 28 competitive performance when compared to pre-existing methods (f-score 0.74). In addition, we effectively recovered associations from an article collection of chronic pulmonary diseases and discovered that a large proportion is not reported in current databases. Our results demonstrate that GeDex, despite its simplicity, is a competitive tool that can successfully assist the curation of existing databases.

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“…Natural language processing (NLP) techniques form one approach to mining the literature. Some researchers have used NLP techniques on PubMed to discover disease-gene associations [13], and others have used PubMed in concert with additional data sources to generate phenotypes [14]. Collier et al used NLP techniques in conjunction with association rule mining to discover phenotypes using PubMed [15].…”

Section: Introductionmentioning

confidence: 99%

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature

Henderson

et al. 2018

J Med Internet Res

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BackgroundResearchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making.ObjectiveThe objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved.MethodsPIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET’s phenotype representation with PheKnow-Cloud’s by using PheKnow-Cloud’s experimental setup. In PIVET’s framework, we also introduce a statistical model trained on domain expert–verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner.ResultsPIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET’s analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes.ConclusionsOur study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

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An analysis of disease-gene relationship from Medline abstracts by DigSee

Cited by 46 publications

References 23 publications

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature

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