A roadmap for multi-omics data integration using deep learning
High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …
amount of multi-omics data for various applications. These data have revolutionized …
Multi-omics integration in biomedical research–A metabolomics-centric review
Recent advances in high-throughput technologies have enabled the profiling of multiple
layers of a biological system, including DNA sequence data (genomics), RNA expression …
layers of a biological system, including DNA sequence data (genomics), RNA expression …
Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
The application of multiple omics technologies in biomedical cohorts has the potential to
reveal patient-level disease characteristics and individualized response to treatment …
reveal patient-level disease characteristics and individualized response to treatment …
A benchmark study of deep learning-based multi-omics data fusion methods for cancer
D Leng, L Zheng, Y Wen, Y Zhang, L Wu, J Wang… - Genome biology, 2022 - Springer
Background A fused method using a combination of multi-omics data enables a
comprehensive study of complex biological processes and highlights the interrelationship of …
comprehensive study of complex biological processes and highlights the interrelationship of …
A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: from computational needs to machine learning and deep learning
Biomarkers, also known as biological markers, are substances like transcripts,
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …
[HTML][HTML] Deep metabolome: Applications of deep learning in metabolomics
In the past few years, deep learning has been successfully applied to various omics data.
However, the applications of deep learning in metabolomics are still relatively low compared …
However, the applications of deep learning in metabolomics are still relatively low compared …
Metabolomics and multi-omics integration: a survey of computational methods and resources
As researchers are increasingly able to collect data on a large scale from multiple clinical
and omics modalities, multi-omics integration is becoming a critical component of …
and omics modalities, multi-omics integration is becoming a critical component of …
The application of artificial neural networks in metabolomics: a historical perspective
Background Metabolomics data, with its complex covariance structure, is typically modelled
by projection-based machine learning (ML) methods such as partial least squares (PLS) …
by projection-based machine learning (ML) methods such as partial least squares (PLS) …
[HTML][HTML] Evidence for human milk as a biological system and recommendations for study design—a report from “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN …
Human milk contains all of the essential nutrients required by the infant within a complex
matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is …
matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is …
Deep learning-based approaches for multi-omics data integration and analysis
Background The rapid growth of deep learning, as well as the vast and ever-growing
amount of available data, have provided ample opportunity for advances in fusion and …
amount of available data, have provided ample opportunity for advances in fusion and …