AIMC Topic: DNA

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Predicting DNA Reactions with a Quantum Chemistry-Based Deep Learning Model.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self-designed descriptor matrices, the model...

Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice.

Genes
The introduction of PCR into forensic science and the rapid increases in the sensitivity, specificity and discrimination power of DNA profiling that followed have been fundamental in shaping the field of forensic biology. Despite these developments, ...

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Methods (San Diego, Calif.)
Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protei...

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Journal of the American Chemical Society
Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as D...

Deciphering the Language of Protein-DNA Interactions: A Deep Learning Approach Combining Contextual Embeddings and Multi-Scale Sequence Modeling.

Journal of molecular biology
Deciphering the mechanisms governing protein-DNA interactions is crucial for understanding key cellular processes and disease pathways. In this work, we present a powerful deep learning approach that significantly advances the computational predictio...

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning.

Scientific data
We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These mo...

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Methods (San Diego, Calif.)
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by ...

Machine Learning-Assisted Near-Infrared Spectral Fingerprinting for Macrophage Phenotyping.

ACS nano
Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coup...

Deep learning based method for predicting DNA N6-methyladenosine sites.

Methods (San Diego, Calif.)
DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intens...

Geometric deep learning of protein-DNA binding specificity.

Nature methods
Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding sp...