AIMC Topic: DNA
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Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement.
IEEE transactions on neural networks and learning systems
May 2, 2025
Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the a...
High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH.
Cell
Apr 15, 2025
Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or r...
Design and Implementation of Pavlovian Associative Memory Based on DNA Neurons.
IEEE transactions on neural networks and learning systems
Apr 4, 2025
In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and thr...
Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game.
Journal of chemical information and modeling
Mar 30, 2025
Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life's biomolecules. We performed oligonucleotide structure prediction and...
Machine Learning-Driven Quantum Sequencing of Natural and Chemically Modified DNA.
ACS applied materials & interfaces
Mar 29, 2025
Simultaneous identification of natural and chemically modified DNA nucleotides at molecular resolution remains a pivotal challenge in genomic science. Despite significant advances in current sequencing technologies, the ability to identify subtle cha...
CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.
BMC genomics
Mar 18, 2025
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...
HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.
Methods (San Diego, Calif.)
Feb 8, 2025
Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gai...
Deep Learning and Single-Molecule Localization Microscopy Reveal Nanoscopic Dynamics of DNA Entanglement Loci.
ACS nano
Feb 4, 2025
Understanding molecular dynamics at the nanoscale remains challenging due to limitations in the temporal resolution of current imaging techniques. Deep learning integrated with Single-Molecule Localization Microscopy (SMLM) offers opportunities to pr...
Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.
The journal of physical chemistry letters
Jan 23, 2025
Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study intro...
Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.
Genes
Jan 19, 2025
BACKGROUND/OBJECTIVES: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve pro...
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