AIMC Topic: Base Sequence

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Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics Nov 30, 2023
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...
Gene Expression Benchmarking Neural Networks, Computer DNA Base Sequence Humans
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ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction.

Biomolecules Nov 13, 2023
This paper presents ConF, a novel deep learning model designed for accurate and efficient prediction of noncoding RNA families. NcRNAs are essential functional RNA molecules involved in various cellular processes, including replication, transcription...
Deep Learning Base Sequence RNA, Untranslated Humans
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Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Biophysical journal Oct 14, 2023
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA s...
RNA Base Sequence Nucleic Acid Conformation Neural Networks, Computer Molecular Dynamics Simulation
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Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology Aug 4, 2023
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...
Nucleotides RNA RNA, Viral Alleles Binding Sites Nucleotide Motifs Datasets as Topic Base Sequence Deep Learning RNA, Messenger Humans RNA-Binding Proteins RNA Splice Sites Computer Simulation Consensus Sequence Bias Mutation Internet
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CRMSNet: A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.

Proteins Mar 28, 2023
RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA-protein binding sites. Deep learning algorithms based on tradit...
Neural Networks, Computer Deep Learning RNA RNA-Binding Proteins Base Sequence
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SCP4ssd: A Serverless Platform for Nucleotide Sequence Synthesis Difficulty Prediction Using an AutoML Model.

Genes Feb 28, 2023
DNA synthesis is widely used in synthetic biology to construct and assemble sequences ranging from short RBS to ultra-long synthetic genomes. Many sequence features, such as the GC content and repeat sequences, are known to affect the synthesis diffi...
Machine Learning Base Sequence Base Composition Escherichia coli DNA
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A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

International journal of molecular sciences Nov 17, 2022
Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among th...
Algorithms RNA, Small Interfering Base Sequence Neural Networks, Computer Machine Learning
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Minimum Functional Length Analysis of K-Mer Based on BPNN.

IEEE/ACM transactions on computational biology and bioinformatics Oct 10, 2022
BP neural network (BPNN), as a multilayer feed-forward network, can realize the deep cognition to target data and high accuracy to output results. However, there were still no related research of k-mer based on BPNN yet. In present study, BPNN was us...
Base Sequence Nucleic Acids Algorithms Sequence Analysis, DNA Neural Networks, Computer
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DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2'-O-Dimethyladenosine Sites in RNA Sequences.

International journal of molecular sciences Sep 20, 2022
N6,2'-O-dimethyladenosine (mAm) is a post-transcriptional modification that may be associated with regulatory roles in the control of cellular functions. Therefore, it is crucial to accurately identify transcriptome-wide mAm sites to understand under...
RNA, Messenger Nucleotides Algorithms Base Sequence Deep Learning
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Context dependent prediction in DNA sequence using neural networks.

PeerJ Sep 20, 2022
One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural n...
DNA Animals Humans Genome, Human Base Sequence Neural Networks, Computer Mice Nucleosomes
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