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Pleiotropy01:33

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Updated: Jul 1, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Sareh Amerifar1,2,3, Andreas Kopf3,4,5, Steffen Sass6,7

  • 1MedicineII-Hematology and Oncology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Hessen, Germany.

Briefings in Bioinformatics
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

JOANA, a novel Bayesian framework, enhances pathway enrichment analysis for omics data by reducing false discoveries and integrating multi-omics information. This method improves biological insight extraction from complex datasets, offering higher specificity and sensitivity.

Keywords:
Bayesian modelingbioinformaticsmachine learningmulti-omics integrationpathway enrichment analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pathway enrichment analysis is crucial for interpreting omics data.
  • Existing methods face limitations like high false discovery rates and poor multi-omics integration.

Purpose of the Study:

  • Introduce JOANA (Joint continuous multi-Omics enrichment ANAlysis), a Bayesian framework for robust pathway analysis.
  • Address limitations of current methods for omics data interpretation.

Main Methods:

  • Utilize continuous probabilistic modeling with Beta mixture distributions for significance scoring.
  • Employ Bayesian networks for multi-omics data integration, handling missing values.
  • Demonstrate versatility across transcriptomics, proteomics, single-cell, mutation, and epigenomics data.

Main Results:

  • JOANA achieves high specificity by eliminating arbitrary thresholds.
  • Integrates multi-omics data, revealing pathways missed by single-layer analyses.
  • Shows up to a 20-fold reduction in reported pathways compared to existing methods while maintaining sensitivity.

Conclusions:

  • JOANA offers a versatile and sensitive framework for multi-omics pathway enrichment analysis.
  • The open-source Python package 'joanapy' facilitates its application.
  • JOANA improves biological insight extraction from complex omics datasets.