Deep learning for healthcare: review, opportunities and challenges
Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …
heterogeneous biomedical data remains a key challenge in transforming health care …
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 …
Current progress and open challenges for applying deep learning across the biosciences
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand
challenges in computational biology: the half-century-old problem of protein structure …
challenges in computational biology: the half-century-old problem of protein structure …
Multi-omics data integration, interpretation, and its application
I Subramanian, S Verma, S Kumar… - … and biology insights, 2020 - journals.sagepub.com
To study complex biological processes holistically, it is imperative to take an integrative
approach that combines multi-omics data to highlight the interrelationships of the involved …
approach that combines multi-omics data to highlight the interrelationships of the involved …
Integrated genomic characterization of pancreatic ductal adenocarcinoma
We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic
ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low …
ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low …
[HTML][HTML] Chromatin potential identified by shared single-cell profiling of RNA and chromatin
Cell differentiation and function are regulated across multiple layers of gene regulation,
including modulation of gene expression by changes in chromatin accessibility. However …
including modulation of gene expression by changes in chromatin accessibility. However …
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
To fully utilize the advances in omics technologies and achieve a more comprehensive
understanding of human diseases, novel computational methods are required for integrative …
understanding of human diseases, novel computational methods are required for integrative …
[HTML][HTML] Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI
AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
Intertumoral heterogeneity within medulloblastoma subgroups
While molecular subgrou** has revolutionized medulloblastoma classification, the extent
of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to …
of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to …
Guidelines for the use of flow cytometry and cell sorting in immunological studies
These guidelines are a consensus work of a considerable number of members of the
immunology and flow cytometry community. They provide the theory and key practical …
immunology and flow cytometry community. They provide the theory and key practical …