AIMC Topic: Nucleotide Motifs

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Prediction of DNA i-motifs via machine learning.

Nucleic acids research Mar 21, 2024
i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of...
Base Sequence Nucleotide Motifs Cytosine Humans Machine Learning DNA
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DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

Bioinformatics (Oxford, England) Feb 1, 2024
MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through intera...
RNA-Binding Proteins RNA Nucleotide Motifs Mice Humans Computational Biology Animals RNA, Messenger Deep Learning
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ScanNet uncovers binding motifs in protein structures with deep learning.

Nature methods Jun 1, 2022
Binding Sites Nucleotide Motifs Amino Acid Motifs Deep Learning Protein Binding
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DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs.

Briefings in bioinformatics Jan 17, 2022
The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over ...
Humans Deep Learning Protein Binding Nucleotide Motifs Transcription Factors Binding Sites
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DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Briefings in bioinformatics Nov 5, 2021
Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDN...
Liquid Biopsy DNA, Neoplasm Organ Specificity Nucleotide Motifs Early Detection of Cancer Neoplasm Staging High-Throughput Nucleotide Sequencing DNA Methylation Cell-Free Nucleic Acids Humans Neoplasms Deep Learning Computational Biology Sequence Analysis, DNA
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Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Briefings in bioinformatics Nov 5, 2021
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif ide...
Genomics Computational Biology Benchmarking Software Algorithms Deep Learning High-Throughput Nucleotide Sequencing Nucleotide Motifs Amino Acid Motifs Humans Neural Networks, Computer
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Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research Sep 20, 2021
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...
RNA Processing, Post-Transcriptional Humans Nucleotide Motifs Adenosine Mouse Embryonic Stem Cells Machine Learning RNA-Seq HEK293 Cells Sensitivity and Specificity Mice Methyltransferases Animals RNA, Messenger
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A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics Sep 2, 2021
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...
Humans Neural Networks, Computer Software MCF-7 Cells Datasets as Topic CCCTC-Binding Factor K562 Cells Nucleotide Motifs Chromatin Gene Expression Regulation Genome, Human Molecular Conformation
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A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research Jul 21, 2021
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...
Promoter Regions, Genetic Genomics Arabidopsis Humans Nucleotide Motifs Transcription Factors Chromatin Immunoprecipitation Sequencing Deep Learning Regulatory Elements, Transcriptional Cell Line
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A survey on deep learning in DNA/RNA motif mining.

Briefings in bioinformatics Jul 20, 2021
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and managemen...
RNA Nucleotide Motifs Neural Networks, Computer Sequence Analysis, RNA Sequence Analysis, DNA DNA
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