Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
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 …
A benchmark study of deep learning-based multi-omics data fusion methods for cancer
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 …
Dealing with dimensionality: the application of machine learning to multi-omics data
Motivation Machine learning (ML) methods are motivated by the need to automate
information extraction from large datasets in order to support human users in data-driven …
information extraction from large datasets in order to support human users in data-driven …
Survey on multi-omics, and multi-omics data analysis, integration and application
MH Shahrajabian, W Sun - Current Pharmaceutical Analysis, 2023 - benthamdirect.com
Multi-omics approaches have developed as a profitable technique for plant systems, a
popular method in medical and biological sciences underlining the necessity to outline new …
popular method in medical and biological sciences underlining the necessity to outline new …
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Cancer is a complex disease that deregulates cellular functions at various molecular levels
(eg, DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is …
(eg, DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is …
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine
Y Li, L Ma, D Wu, G Chen - Briefings in Bioinformatics, 2021 - academic.oup.com
Multi-omics allows the systematic understanding of the information flow across different
omics layers, while single omics can mainly reflect one aspect of the biological system. The …
omics layers, while single omics can mainly reflect one aspect of the biological system. The …
[HTML][HTML] Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma
Y Lin, Y Yang, K Yuan, S Yang, S Zhang, H Li, T Tang - Bioactive materials, 2022 - Elsevier
Current in vitro models for osteosarcoma investigation and drug screening, including two-
dimensional (2D) cell culture and tumour spheroids (ie cancer stem-like cells), lack …
dimensional (2D) cell culture and tumour spheroids (ie cancer stem-like cells), lack …
Application of deep learning in cancer epigenetics through DNA methylation analysis
DNA methylation is a fundamental epigenetic modification involved in various biological
processes and diseases. Analysis of DNA methylation data at a genome-wide and high …
processes and diseases. Analysis of DNA methylation data at a genome-wide and high …
Deep reinforced neural network model for cyto-spectroscopic analysis of epigenetic markers for automated oral cancer risk prediction
Understanding epigenetic changes can provide vital information for early stage oral cancer
diagnosis. Vibrational spectroscopy methods like Raman spectroscopy (RS) and Fourier …
diagnosis. Vibrational spectroscopy methods like Raman spectroscopy (RS) and Fourier …