CADA: Phenotype-driven gene prioritization based on a case-enriched knowledge graph

AbstractMany rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the human phenotype ontology (HPO), which is increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed, however, the performance of many such tools suffers from an overrepresentation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With CADA we built a knowledge-graph that is based on case annotations and disorder annotations and show that CADA exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Crucial in the design of our approach is the use of the growing amount of phenotypic information that diagnostic labs deposit in databases such as ClinVar. By this means CADA is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.

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CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab078 ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Chengyao Peng ◽

Simon Dieck ◽

Alexander Schmid ◽

Ashar Ahmad ◽

Alexej Knaus ◽

...

Keyword(s):

Representation Learning ◽

Gene Prioritization ◽

Superior Performance ◽

Differential Diagnostics ◽

Knowledge Graph ◽

Rare Disorders ◽

Human Phenotype ◽

Reduced Frequency ◽

Genotype Information ◽

Reference Tool

Abstract Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype–genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.

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‘Precision Health’: Balancing Reactive Care and Proactive Care Through the Evidence Based Knowledge Graph Constructed from Real-World Electronic Health Records, Disease Trajectories, Diseasome, and Patholome

Big Data Analytics - Lecture Notes in Computer Science ◽

10.1007/978-3-030-66665-1_9 ◽

2020 ◽

pp. 113-133

Author(s):

Asoke K Talukder ◽

Julio Bonis Sanz ◽

Jahnavi Samajpati

Keyword(s):

Electronic Health Records ◽

Real World ◽

Knowledge Graph ◽

Evidence Based ◽

Health Records ◽

Electronic Health ◽

Proactive Care ◽

Precision Health

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Towards Identifying Author Confidence in Biomedical Articles

Data ◽

10.3390/data4010018 ◽

2019 ◽

Vol 4 (1) ◽

pp. 18 ◽

Cited By ~ 1

Author(s):

Mihaela Onofrei Plămadă ◽

Diana Trandabăț ◽

Daniela Gîfu

Keyword(s):

Medical Literature ◽

Academic Research ◽

Biomedical Text Mining ◽

Huge Amount ◽

Health Records ◽

Effective Dissemination ◽

First Impression ◽

Series Of Experiments ◽

Using Data ◽

Open Archives

In an era where the volume of medical literature is increasing daily, researchers in the biomedical and clinical areas have joined efforts with language engineers to analyze the large amount of biomedical and molecular biology literature (such as PubMed), patient data, or health records. With such a huge amount of reports, evaluating their impact has long stopped being a trivial task. In this context, this paper intended to introduce a non-scientific factor that represents an important element in gaining acceptance of claims. We postulated that the confidence that an author has in expressing their work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper were based on a series of experiments that were ran using data from the open archives initiative (OAI) corpus, which provides interoperability standards to facilitate effective dissemination of the content. This method may be useful to the direct beneficiaries (i.e., authors, who are engaged in medical or academic research), but also, to the researchers in the fields of biomedical text mining (BioNLP) and NLP, etc.

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Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Scientific Reports ◽

10.1038/s41598-018-22521-4 ◽

2018 ◽

Vol 8 (1) ◽

Author(s):

Daniel M. Bean ◽

Honghan Wu ◽

Ehtesham Iqbal ◽

Olubanke Dzahini ◽

Zina M. Ibrahim ◽

...

Keyword(s):

Electronic Health Records ◽

Adverse Drug Reactions ◽

Knowledge Graph ◽

Drug Reactions ◽

Health Records ◽

Electronic Health

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Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

Frontiers in Blockchain ◽

10.3389/fbloc.2021.661238 ◽

2021 ◽

Vol 4 ◽

Author(s):

Yao Yao ◽

Meghana Kshirsagar ◽

Gauri Vaidya ◽

Jens Ducrée ◽

Conor Ryan

Keyword(s):

Electronic Health Records ◽

Autonomous Agents ◽

Knowledge Integration ◽

Clinical Decision Making ◽

User Authentication ◽

Clinical Decision ◽

Knowledge Graph ◽

Autonomous Agent ◽

Health Records ◽

Electronic Health

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

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Phen2Gene: Rapid Phenotype-Driven Gene Prioritization for Rare Diseases

10.1101/870527 ◽

2019 ◽

Author(s):

Mengge Zhao ◽

James M. Havrilla ◽

Li Fang ◽

Ying Chen ◽

Jacqueline Peng ◽

...

Keyword(s):

Rare Diseases ◽

Probabilistic Model ◽

Gene List ◽

Genetic Diagnosis ◽

Great Accuracy ◽

Gene Prioritization ◽

Patient Specific ◽

Annotation Database ◽

Human Phenotype ◽

Link Type

AbstractHuman Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases, and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPackets descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model, and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed, and acts as a real-time phenotype driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients’ de-identified HPO term data from CHOP.

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Boerhaave’s syndrome: case report and review

Clinical Practice ◽

10.17816/clinpract09163-70 ◽

2018 ◽

Vol 9 (1) ◽

pp. 63-70

Author(s):

S. A. Andreychenko ◽

M. V. Bychinin ◽

T. V. Klypa ◽

Yu. V. Ivanov ◽

D. V. Sazonov ◽

...

Keyword(s):

Myocardial Infarction ◽

Medical Literature ◽

Differential Diagnostics ◽

Clinical Implications ◽

Boerhaave’S Syndrome ◽

Surgical Tools ◽

Boerhaave's Syndrome ◽

Artery Embolism ◽

The Moment ◽

Pulmonary Artery Embolism

Well-timed diagnostics of a spontaneous nontraumatic rupture of esophagus or Boerhaave’s syndrome, presents great difficulties because of his rarity and a variety of clinical implications. Esophagus ruptures may feign various organs pathology [2] that most often demands differential diagnostics with a stomach ulcer perforation, acute myocardial infarction, pulmonary artery embolism, aortic dissection and pancreatitis [16, 17]. The treatment can include conservative and surgical tools, but still accompanied by high mortality (up to 35%) [7]; results largely defined by the time between the moment of a rupture and start of the treatment. In addition to the review, described the experience of successful treatment of a patient with Boerhaave’s syndrome in the light of the generalized today data of world medical literature on this problem.

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Uncommon conditions

10.1093/med/9780198719410.003.0013 ◽

2016 ◽

Author(s):

Graham Hocking

Keyword(s):

Medical Literature ◽

Rare Disorders ◽

Comprehensive List ◽

Full Discussion

This chapter provides a series of vignettes of over 200 rare disorders, including many congenital syndromes. For reasons of space, a full discussion cannot be provided for each, but the most pertinent anaesthetic considerations are described, and a comprehensive list of recent references from the medical literature is provided.

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Doc2Hpo: a web application for efficient and accurate HPO concept curation

Nucleic Acids Research ◽

10.1093/nar/gkz386 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W566-W570 ◽

Cited By ~ 5

Author(s):

Cong Liu ◽

Fabricio Sampaio Peres Kury ◽

Ziran Li ◽

Casey Ta ◽

Kai Wang ◽

...

Keyword(s):

Real Time ◽

Web Application ◽

Source Code ◽

High Accuracy ◽

Health Data ◽

Gene Prioritization ◽

Phenotype Ontology ◽

Human Phenotype ◽

Clinical Text ◽

Local Installation

AbstractWe present Doc2Hpo, an interactive web application that enables interactive and efficient phenotype concept curation from clinical text with automated concept normalization using the Human Phenotype Ontology (HPO). Users can edit the HPO concepts automatically extracted by Doc2Hpo in real time, and export the extracted HPO concepts into gene prioritization tools. Our evaluation showed that Doc2Hpo significantly reduced manual effort while achieving high accuracy in HPO concept curation. Doc2Hpo is freely available at https://impact2.dbmi.columbia.edu/doc2hpo/. The source code is available at https://github.com/stormliucong/doc2hpo for local installation for protected health data.

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