Discover Community Create You

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

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method.

Proceedings of the National Academy of Sciences of the United States of America - 6 Feb 2018

Kim Byung-Ju, Kim Sung-Hou

Abstract excerpt

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual's susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the...

Read the complete abstract on PubMed

Topics

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

Share to Genetic Predisposition to Disease
Share to Genome, Human

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Life Style

Share to Machine Learning

Share to Neoplasms

Share to Polymorphism, Single Nucleotide

Share to Probability

Source

Open in PubMed Open full text

Identifiers

PMID: 29358382
DOI: 10.1073/pnas.1717960115
PMCID: PMC5819441

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method.PMID 29358382Read ↗

Empirical prediction of genomic susceptibilities for multiple cancer classes.Semantic proximity Read ↗

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk predictionSemantic proximity Read ↗

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.Semantic proximity Read ↗

Genetic Risk for Overall Cancer and the Benefit of Adherence to a Healthy Lifestyle.Semantic proximity Read ↗

Cancer Diagnosis, Polygenic Risk, and Longevity-Associated VariantsSemantic proximity Read ↗

Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobankSemantic proximity Read ↗

The landscape of interactions between cancer polygenic risk scores and somatic alterations in cancer cellsSemantic proximity Read ↗

Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformaticsSemantic proximity Read ↗

Statistical methods for Mendelian models with multiple genes and cancers.Semantic proximity Read ↗

Inequality in genetic cancer risk suggests bad genes rather than bad luck.Semantic proximity Read ↗

Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank.Semantic proximity Read ↗

Multiple polygenic score approach in colorectal cancer risk prediction.Semantic proximity Read ↗

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