Recent methodology progress of deep learning for RNA–protein interaction prediction
Interactions between RNAs and proteins play essential roles in many important biological
processes. Benefitting from the advances of next generation sequencing technologies …
processes. Benefitting from the advances of next generation sequencing technologies …
A systematic benchmark of machine learning methods for protein–RNA interaction prediction
M Horlacher, G Cantini, J Hesse… - Briefings in …, 2023 - academic.oup.com
RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation.
Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in …
Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in …
Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular
regulation, and dynamically reflect specific cellular conditions. However, presently available …
regulation, and dynamically reflect specific cellular conditions. However, presently available …
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks
PK Koo, A Majdandzic, M Ploenzke… - PLoS computational …, 2021 - journals.plos.org
Deep neural networks have demonstrated improved performance at predicting the
sequence specificities of DNA-and RNA-binding proteins compared to previous methods …
sequence specificities of DNA-and RNA-binding proteins compared to previous methods …
[HTML][HTML] Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction
JS Wekesa, J Meng, Y Luan - Genomics, 2020 - Elsevier
Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes
through diverse molecular mechanisms including binding to RNA binding proteins. The …
through diverse molecular mechanisms including binding to RNA binding proteins. The …
A deep learning model for plant lncRNA-protein interaction prediction with graph attention
JS Wekesa, J Meng, Y Luan - Molecular Genetics and Genomics, 2020 - Springer
Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles
through interactions with proteins. However, only a few plant lncRNAs have been …
through interactions with proteins. However, only a few plant lncRNAs have been …
[HTML][HTML] Incorporating biological structure into machine learning models in biomedicine
J Crawford, CS Greene - Current opinion in biotechnology, 2020 - Elsevier
In biomedical applications of machine learning, relevant information often has a rich
structure that is not easily encoded as real-valued predictors. Examples of such data include …
structure that is not easily encoded as real-valued predictors. Examples of such data include …
RNA-binding protein recognition based on multi-view deep feature and multi-label learning
RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in
regulating biological processes. An RBP may have multiple target RNAs, and its aberrant …
regulating biological processes. An RBP may have multiple target RNAs, and its aberrant …
Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction
Predicting RNA-binding protein (RBP) specificity is important for understanding gene
expression regulation and RNA-mediated enzymatic processes. It is widely believed that …
expression regulation and RNA-mediated enzymatic processes. It is widely believed that …
Identifying regulatory elements via deep learning
M Barshai, E Tripto, Y Orenstein - Annual Review of Biomedical …, 2020 - annualreviews.org
Deep neural networks have been revolutionizing the field of machine learning for the past
several years. They have been applied with great success in many domains of the …
several years. They have been applied with great success in many domains of the …