AIMC Topic: Multiomics

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Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

IEEE journal of biomedical and health informatics
With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, the...

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.

The British journal of radiology
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) ...

-derived postbiotics inhibited digestion of triglycerides, glycerol phospholipids and sterol lipids allosteric regulation of BSSL, PTL and PLA2 to prevent obesity: perspectives on deep learning integrated multi-omics.

Food & function
The anti-obesity potential of probiotics has been widely reported, however their utilization in high-risk patients and potential adverse reactions have led researchers to focus their attention on postbiotics. Herein, pseudo-targeted lipidomics linked...

Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine.

International journal of oncology
Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combinati...

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Future oncology (London, England)
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neura...

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Computers in biology and medicine
Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tool...

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

Computers in biology and medicine
The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational ...

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin...

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

European radiology
OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional sta...