Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying Genotypes and Phenotypes of Cardiovascular Diseases Using Big Data Analytics.

JAMA cardiology - 1 Oct 2017

Krittanawong Chayakrit, Kitai Takeshi

Abstract excerpt

No abstract is available from the source.

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cardiomyopathy, Hypertrophic
Share to Cardiovascular Diseases
Share to Genotype
Share to Mutation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Source

Identifiers

PMID: 28614586
DOI: 10.1001/jamacardio.2017.1857

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying Genotypes and Phenotypes of Cardiovascular Diseases Using Big Data Analytics.PMID 28614586Read ↗

The application of big data to cardiovascular disease: paths to precision medicine.Semantic proximity Read ↗

Big Data in Cardiology.Semantic proximity Read ↗

Advancing cardiovascular disease risk prediction beyond conventional methods: a systematic review of multimodal machine learning models integrating traditional clinical factors and multi-omics dataSemantic proximity Read ↗

Genomics of cardiovascular disease.Semantic proximity Read ↗

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicineSemantic proximity Read ↗

Insight into risk associated phenotypes behind COVID-19 from phenotype genome-wide association studiesSemantic proximity Read ↗

Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine LearningSemantic proximity Read ↗

A new era of cardiovascular disease epidemiology.Semantic proximity Read ↗

Bridging Genomics to Cardiology Clinical PracticeSemantic proximity Read ↗

Genome-wide association studies of cardiovascular disease.Semantic proximity Read ↗

Predicting coronary artery disease severity through genomic profiling and machine learning modelling: The GEnetic SYNTAX Score (GESS) trialSemantic proximity Read ↗

Phenotypes of Cardiovascular Diseases: Current Status and Future PerspectivesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.