Article

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data

2023-05-02

Abstract excerpt

<title>Abstract</title> <p>Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5ace2e4f-a106-5798-b8d0-b37007a6e915
DOI: 10.21203/rs.3.rs-2768563/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics dataDOI 10.21203/rs.3.rs-2768563/v1Read

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.Semantic proximity Read

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networksSemantic proximity Read

MOCAT: Multi-Omics Integration with Auxiliary Classifiers Enhanced AutoencoderSemantic proximity Read

A cross-level information transmission network for hierarchical omics data integration and phenotype prediction from a new genotype.Semantic proximity Read

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classificationSemantic proximity Read

Deep Learning Architectures for Multi-Omics Data Integration: Bridging Biomarker Discovery and Clinical TranslationSemantic proximity Read

A benchmark study of deep learning-based multi-omics data fusion methods for cancerSemantic proximity Read

Integrating multi-omics data through deep learning for accurate cancer prognosis predictionSemantic proximity Read

Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding frameworkSemantic proximity Read

AttOmics: attention-based architecture for diagnosis and prognosis from omics data.Semantic proximity Read

MOGAT: An Improved Multi-Omics Integration Framework Using Graph Attention NetworksSemantic proximity Read

Multi-Omic Graph Diagnosis (MOGDx) : A data integration tool to perform classification tasks for heterogeneous diseasesSemantic proximity Read