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

Crohn disease risk prediction-Best practices and pitfalls with exome data.

Human mutation - 1 Sept 2017

Giollo Manuel, Jones David T, Carraro Marco, Leonardi Emanuela, Ferrari Carlo, Tosatto Silvio C E

Abstract excerpt

The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Crohn Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Exome Sequencing

Share to Genetic Predisposition to Disease

Share to High-Throughput Nucleotide Sequencing

Share to Practice Guidelines as Topic

Source

Open in PubMed Open full text

Identifiers

PMID: 28087895
DOI: 10.1002/humu.23177
PMCID: PMC5509518

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Crohn disease risk prediction-Best practices and pitfalls with exome data.PMID 28087895Read ↗

CAGI4 Crohn's exome challenge: Marker SNP versus exome variant models for assigning risk of Crohn disease.Semantic proximity Read ↗

Identifying Crohn’s disease signal from variome analysisSemantic proximity Read ↗

An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn’s disease patientsSemantic proximity Read ↗

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping dataSemantic proximity Read ↗

Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease.Semantic proximity Read ↗

Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibilitySemantic proximity Read ↗

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.Semantic proximity Read ↗

The promise and perils of interpreting genetic associations in Crohn's disease.Semantic proximity Read ↗

Polygenic risk scores for Crohn's disease risk prediction in a Chinese population: insights from multi-ethnic genome-wide association studies.Semantic proximity Read ↗

Opportunities and challenges of whole-genome and -exome sequencingSemantic proximity Read ↗

Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing DataSemantic proximity Read ↗

Exploring the genetic architecture of inflammatory bowel disease by whole genome sequencing identifies association at <i>ADCY7</i>Semantic proximity Read ↗

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