Article

MOCAT: Multi-Omics Integration with Auxiliary Classifiers Enhanced Autoencoder

2023-12-22

Abstract excerpt

<h4>Background</h4> Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data. <h4>Methods</h4> We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoder...

Topics

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

Identifiers and source

Literature Corpus work: 22c2a4ad-41b7-535f-9286-607840931d3d
DOI: 10.1101/2023.12.20.23300334

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

MOCAT: Multi-Omics Integration with Auxiliary Classifiers Enhanced AutoencoderDOI 10.1101/2023.12.20.23300334Read

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

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictionsSemantic proximity Read

Self-Normalizing Deep Learning for Enhanced Multi-Omics Data Analysis in OncologySemantic proximity Read

Interpretable Deep Learning-Based Multi-Omics Integration for Prognosis in Hepatocellular CarcinomaSemantic proximity Read

Deep learning–driven multi-omics analysis: enhancing cancer diagnostics and therapeuticsSemantic proximity Read

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics dataSemantic proximity Read

A biphasic Deep Semi-supervised framework for Suptype Classification and biomarker discoverySemantic proximity Read

MOT: a Multi-Omics Transformer for multiclass classification tumour types predictionsSemantic proximity Read

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

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

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