A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Hashemikhabir, Seyedsasan; Xia, Ran; Xiang, Yang; Janga, Sarath Chandra

doi:10.1109/tcbb.2015.2480056

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…In one example, MalaCards was used to assist transferring annotations from one ontology (the ICD code) to another (GO), aiming at integrating large-scale heterogeneous biomedical ontologies based on genomic relationships (38). The authors reconstructed a merged tree of GO and ICD9 codes and positively assessed it by comparing it with two disease-gene data sets (MalaCards and DO).…”

Section: Use Casesmentioning

confidence: 99%

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

et al. 2016

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The MalaCards human disease database (http://www.malacards.org/) is an integrated compendium of annotated diseases mined from 68 data sources. MalaCards has a web card for each of ∼20 000 disease entries, in six global categories. It portrays a broad array of annotation topics in 15 sections, including Summaries, Symptoms, Anatomical Context, Drugs, Genetic Tests, Variations and Publications. The Aliases and Classifications section reflects an algorithm for disease name integration across often-conflicting sources, providing effective annotation consolidation. A central feature is a balanced Genes section, with scores reflecting the strength of disease-gene associations. This is accompanied by other gene-related disease information such as pathways, mouse phenotypes and GO-terms, stemming from MalaCards’ affiliation with the GeneCards Suite of databases. MalaCards’ capacity to inter-link information from complementary sources, along with its elaborate search function, relational database infrastructure and convenient data dumps, allows it to tackle its rich disease annotation landscape, and facilitates systems analyses and genome sequence interpretation. MalaCards adopts a ‘flat’ disease-card approach, but each card is mapped to popular hierarchical ontologies (e.g. International Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.

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Section: Use Casesmentioning

confidence: 99%

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

et al. 2016

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Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

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Ontology generation is a process of relationship analysis, and representation for multiple data categories using automatic or semi-automatic approaches. This process requires a domain knowledgebase that describes given input data using entity-to-entity relations. A wide variety of approaches are proposed for this purpose, and each of them processes & converts input data using multiple relationship evaluation stages. These stages include data-preprocessing, correlation analysis, entity mapping, and ontology generation. A very few of these approaches are dataset independent, and most of them do not implement security measures during ontology generation, which limits their security, scalability & deployment capabilities during real-time implementation. Thus, in this text a blockchain based secure & e cient ontology generation model for multiple data genres using augmented strati cation (BOGMAS) is described. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. It uses a variance-based method (VBM) for reduction of redundant numerical features from the dataset, while textual features are converted to numerical values via standard word2vec model, and then processed using VBM. This model uses a combination of linear support vector machine (LSVM), and extra trees (ET) strati ers for variance estimation, which makes the model highly e cient, and reduces redundant features from the output ontology. These feature sets & their variances are given to a correlation engine for relationship estimation, and ontology generation.Each ontology record is secured using a mutable proof-of-work (PoW) based blockchain model, which assists in imbibing transparency, traceability, and distributed peer-to-peer processing capabilities. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending upon incoming data. Performance of the proposed BOGMAS model is evaluated in terms of precision & recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly e cient in terms of precision, recall & accuracy performance, but has incrementally higher computational complexity & delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things (IoT) ontology generation with high security levels.

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Automated generation of decision-tree models for the economic assessment of interventions for rare diseases using the RaDiOS ontology

Prieto-González

Castilla-Rodríguez

González

et al. 2020

Journal of Biomedical Informatics

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A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Cited by 5 publications

References 33 publications

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Automated generation of decision-tree models for the economic assessment of interventions for rare diseases using the RaDiOS ontology

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