National Cancer Institute Thesaurus (NCIt)

Coronado, Sherri de; Remennik, Lyubov; Elkin, Peter L.

doi:10.1007/978-3-031-11039-9_17

Cited by 2 publications

(1 citation statement)

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“…Additionally, ANNOVAR [46] was employed for annotating the functional effects of variations. Disease metadata, including name, definition, and ontology structure, were harmonized using OncoTree (http://oncotree.mskcc.org/) [47], Mesh (https://meshb-prev.nlm.nih.gov/search) [48], and the NCI Thesaurus (NCIt, https://ncithesaurus.nci.nih.gov/ncitbrowser/) [49,50].…”

Section: Meta Database Constructionmentioning

confidence: 99%

A comprehensive personal omics clinical interpreter based on genomic and transcriptomic profiles

Liu,

Chen,

Chen

et al. 2024

Preprint

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Advances in precision medicine rely on the accurate identification and analysis of molecular alterations for personalized diagnostic, prognostic, and therapeutic decision-making. A critical obstacle is the integration of heterogeneous interpretations of clinically actionable alterations from various knowledgebases. Here, we present the Personal Omics Interpreter (POI), a web-based application engineered to aggregate and interpret therapeutic options, including targeted, immunological, and chemotherapeutic agents, by leveraging personal genomic and transcriptomic profiles. POI employs the Precision Medicine Knowledgebase (PreMedKB), an updated harmonized resource we previously reported, to annotate the clinically actionable somatic variants. It further incorporates a predictive algorithm to broaden therapeutic options according to established gene-gene interactions and offers insights into phenotypic responses of chemotherapeutic agents through phasing germline diplotypes. Validated against three cohort datasets encompassing over 22,000 cancer patients, POI demonstrates consistently high matching rates (94.7 ∼ 95.6%) between patients and suggested therapies, highlighting its potential in supporting precision-driven informed treatment strategies.

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Section: Meta Database Constructionmentioning

confidence: 99%

A comprehensive personal omics clinical interpreter based on genomic and transcriptomic profiles

Liu,

Chen,

Chen

et al. 2024

Preprint

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Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Jha

Mithun

Sherkhane

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in cancer treatment remains low; hence, cancer treatment is gradually shifting toward personalized treatment. Artificial intelligence (AI) and radiomics have been recognized as one of the potential areas of research in personalized medicine in oncology. Several researchers have identified the capabilities of AI and radiomics to characterize phenotype and there by predict the outcome of treatment in oncology. Although AI and radiomics have shown promising initial results in diagnosis and treatment in oncology, these technologies are also facing challenges of standardization and scalability. In the last few years, researchers have been trying to develop a research infrastructure for federated machine learning that increases the usability of Big Data for clinical research. These research infrastructures are based on the findable, accessible, interoperable, and reusable (i.e., FAIR) data principles. The India-Dutch "big imaging data approach for oncology in a Netherlands India collaboration" (BIONIC) is a jointly funded initiative by the Dutch Research Council (NWO) and the Indian Ministry of Electronics and Information Technology (MeitY), aiming to introduce radiomic-based research into clinical environments using federated machine learning on geographically dispersed collections of FAIR data. This article described a prototype end-to-end research infrastructure implemented through the BIONIC partnership into a leading cancer care public hospital in India.

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National Cancer Institute Thesaurus (NCIt)

Cited by 2 publications

References 0 publications

A comprehensive personal omics clinical interpreter based on genomic and transcriptomic profiles

A comprehensive personal omics clinical interpreter based on genomic and transcriptomic profiles

Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

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