Applications of deep learning in understanding gene regulation
Gene regulation is a central topic in cell biology. Advances in omics technologies and the
accumulation of omics data have provided better opportunities for gene regulation studies …
accumulation of omics data have provided better opportunities for gene regulation studies …
Protein–RNA interaction prediction with deep learning: structure matters
Protein–RNA interactions are of vital importance to a variety of cellular activities. Both
experimental and computational techniques have been developed to study the interactions …
experimental and computational techniques have been developed to study the interactions …
Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet
RNA-binding proteins play crucial roles in the regulation of gene expression, and
understanding the interactions between RNAs and RBPs in distinct cellular conditions forms …
understanding the interactions between RNAs and RBPs in distinct cellular conditions forms …
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues
Y Xia, CQ Xia, X Pan, HB Shen - Nucleic acids research, 2021 - academic.oup.com
Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of
understanding various biological activities and designing new drugs. How to accurately …
understanding various biological activities and designing new drugs. How to accurately …
AlphaFold2-aware protein–DNA binding site prediction using graph transformer
Protein–DNA interactions play crucial roles in the biological systems, and identifying protein–
DNA binding sites is the first step for mechanistic understanding of various biological …
DNA binding sites is the first step for mechanistic understanding of various biological …
Detrac: Transfer learning of class decomposed medical images in convolutional neural networks
A Abbas, MM Abdelsamea, MM Gaber - IEEE Access, 2020 - ieeexplore.ieee.org
Due to the high availability of large-scale annotated image datasets, paramount progress
has been made in deep convolutional neural networks (CNNs) for image classification tasks …
has been made in deep convolutional neural networks (CNNs) for image classification tasks …
Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions
Non-coding RNA structure and function are essential to understanding various biological
processes, such as cell signaling, gene expression, and post-transcriptional regulations …
processes, such as cell signaling, gene expression, and post-transcriptional regulations …
Computational tools for aptamer identification and optimization
D Sun, M Sun, J Zhang, X Lin, Y Zhang, F Lin… - TrAC Trends in …, 2022 - Elsevier
Aptamers are single-stranded DNA or RNA oligonucleotides that can selectively bind to a
specific target. They are generally obtained by SELEX, but the procedure is challenging and …
specific target. They are generally obtained by SELEX, but the procedure is challenging and …
Machine learning aided construction of the quorum sensing communication network for human gut microbiota
S Wu, J Feng, C Liu, H Wu, Z Qiu, J Ge, S Sun… - Nature …, 2022 - nature.com
Quorum sensing (QS) is a cell-cell communication mechanism that connects members in
various microbial systems. Conventionally, a small number of QS entries are collected for …
various microbial systems. Conventionally, a small number of QS entries are collected for …
A self-adaptive deep learning algorithm for accelerating multi-component flash calculation
In this paper, the first self-adaptive deep learning algorithm is proposed in details to
accelerate flash calculations, which can quantitatively predict the total number of phases in …
accelerate flash calculations, which can quantitatively predict the total number of phases in …