[HTML][HTML] Big data and deep learning for RNA biology
H Hwang, H Jeon, N Yeo, D Baek - Experimental & Molecular Medicine, 2024 - nature.com
The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …
[HTML][HTML] Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
The incorporation of machine learning methods into proteomics workflows improves the
identification of disease-relevant biomarkers and biological pathways. However, machine …
identification of disease-relevant biomarkers and biological pathways. However, machine …
Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on cytidine diphosphate-diacylglycerol synthase …
X Wu, W Jia - Critical Reviews in Food Science and Nutrition, 2023 - Taylor & Francis
Deep learning is evolving in nutritional epidemiology to address challenges including
precise nutrition and data-driven disease modeling. Fermented dairy products consumption …
precise nutrition and data-driven disease modeling. Fermented dairy products consumption …
Current and future directions in network biology
Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …
sciences, is critical for deepening understanding of cellular functioning and disease. While …
[HTML][HTML] scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data
Although advances in single-cell technologies have enabled the characterization of multiple
omics profiles in individual cells, extracting functional and mechanistic insights from such …
omics profiles in individual cells, extracting functional and mechanistic insights from such …
[HTML][HTML] Contextual AI models for single-cell protein biology
MM Li, Y Huang, M Sumathipala, MQ Liang… - Nature …, 2024 - nature.com
Understanding protein function and developing molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …
cell types in which proteins act as well as the interactions between proteins. However …
[HTML][HTML] Contextualizing protein representations using deep learning on protein networks and single-cell data
Understanding protein function and developing molecular therapies require deciphering the
cell types in which proteins act as well as the interactions between proteins. However …
cell types in which proteins act as well as the interactions between proteins. However …
Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes
While the primary sequences of human proteins have been cataloged for over a decade,
determining how these are organized into a dynamic collection of multiprotein assemblies …
determining how these are organized into a dynamic collection of multiprotein assemblies …
A foundational atlas of autism protein interactions reveals molecular convergence
Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable
treatment targets remains elusive. We constructed a foundational protein-protein interaction …
treatment targets remains elusive. We constructed a foundational protein-protein interaction …
Computational methods for prediction of human protein-phenotype associations: a review
Deciphering the relationship between human proteins (genes) and phenotypes is one of the
fundamental tasks in phenomics research. The Human Phenotype Ontology (HPO) builds …
fundamental tasks in phenomics research. The Human Phenotype Ontology (HPO) builds …