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

Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 1 Aug 2016

Su-Ping Deng, Dongdong Lin, Calhoun Vince D, Yu-Ping Wang

Abstract excerpt

In order to comprehensively utilize complementary information from multiple types of data for better disease diagnosis, in this study, we applied a network fusion based approach to integrating three types of data including genetic, epigenetic and neuroimaging data from a study of schizophrenia patients (SCZ). A network is a map of interactions, which contributes to investigating the connectivity of components or...

Read the complete abstract on PubMed

Topics

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

Share to DNA Methylation
Share to Magnetic Resonance Imaging

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Share to Schizophrenia

Source

Identifiers

PMID: 28268598
DOI: 10.1109/EMBC.2016.7590981

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

Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data.PMID 28268598Read ↗

Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease.Semantic proximity Read ↗

Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.Semantic proximity Read ↗

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method.Semantic proximity Read ↗

Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.Semantic proximity Read ↗

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal RegressionSemantic proximity Read ↗

Dynamic fusion of genomics and functional network connectivity in UK biobank reveals schizophrenia-related SNP manifoldsSemantic proximity Read ↗

Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals.Semantic proximity Read ↗

Integrated analysis supports ATXN1 as a schizophrenia risk gene.Semantic proximity Read ↗

Multimodal Imaging Genetic Analysis of Schizophrenia Based on Sparse Representation and Dictionary LearningSemantic proximity Read ↗

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.Semantic proximity Read ↗

Integration of SNPs-FMRI-methylation data with sparse multi-CCA for schizophrenia study.Semantic proximity Read ↗

Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.Semantic proximity Read ↗

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