AIMC Topic: DNA

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LegNet: a best-in-class deep learning model for short DNA regulatory regions.

Bioinformatics (Oxford, England) Aug 1, 2023
MOTIVATION: The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar.
Regulatory Sequences, Nucleic Acid Promoter Regions, Genetic Software Deep Learning DNA
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Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation.

Artificial life Aug 1, 2023
The design and implementation of adaptive chemical reaction networks, capable of adjusting their behavior over time in response to experience, is a key goal for the fields of molecular computing and DNA nanotechnology. Mainstream machine learning res...
Neural Networks, Computer Computer Simulation DNA Machine Learning Algorithms
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DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Briefings in bioinformatics Jul 20, 2023
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the ...
Neural Networks, Computer Binding Sites Transcription Factors DNA Protein Binding
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Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Briefings in bioinformatics Mar 19, 2023
Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have sho...
Neural Networks, Computer Protein Binding Binding Sites Transcription Factors DNA
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DeepOM: single-molecule optical genome mapping via deep learning.

Bioinformatics (Oxford, England) Mar 1, 2023
MOTIVATION: Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optic...
Restriction Mapping Deep Learning Genomics DNA Genome, Human Humans Software
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DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.

Briefings in bioinformatics Jan 19, 2023
Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular ty...
Nucleotides Epigenesis, Genetic DNA Machine Learning Software
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A review of methods for predicting DNA N6-methyladenine sites.

Briefings in bioinformatics Jan 19, 2023
Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA si...
DNA Machine Learning Research Design DNA Methylation
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Robotic DNA Extraction Utilizing Qiagen BioSprint 96 Workstation.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
After an examination of evidentiary or reference samples has been performed, the next step is DNA extraction. This crucial step allows for deoxyribonucleic acid (DNA) to be released from a substrate by use of a series of chemicals and allows the DNA ...
Saliva DNA Automation Robotics Robotic Surgical Procedures DNA Fingerprinting
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Deep Learning on Chromatin Accessibility.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been show...
Sequence Analysis, DNA DNA Genomics High-Throughput Nucleotide Sequencing Chromatin Deep Learning
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A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes in Individual Tumors.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline...
DNA Methylation DNA Epigenome Deep Learning Child Neoplasms Humans Adult
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