AIMC Topic: DNA

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Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Protein and peptide letters
BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G...

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

Methods in molecular biology (Clifton, N.J.)
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-f...

Design and deep learning of synthetic B-cell-specific promoters.

Nucleic acids research
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synt...

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures.

Briefings in bioinformatics
The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-bas...

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

Bioinformatics (Oxford, England)
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.

Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation.

Artificial life
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...

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Briefings in bioinformatics
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 ...

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Briefings in bioinformatics
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...

DeepOM: single-molecule optical genome mapping via deep learning.

Bioinformatics (Oxford, England)
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...

DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method.

Briefings in bioinformatics
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...