AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

DNA

Showing 261 to 270 of 429 articles

Clear Filters

Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.

Scientific reports
Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA bindin...

An Intelligent DNA Nanorobot with Enhanced Protein Lysosomal Degradation of HER2.

Nano letters
DNA nanorobots have emerged as new tools for nanomedicine with the potential to ameliorate the delivery and anticancer efficacy of various drugs. DNA nanostructures have been considered one of the most promising nanocarriers. In the present study, we...

DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

Journal of computer-aided molecular design
DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structu...

DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines.

Journal of chemical information and modeling
Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the prediction of protein-DNA binding sites. However, the data ...

Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.

Molecules (Basel, Switzerland)
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability t...

Predicting DNA Methylation States with Hybrid Information Based Deep-Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics
DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, there are several sequence-based deep learning models designed to predict DNA methylation states, whic...

Machine Learning Prediction of DNA Charge Transport.

The journal of physical chemistry. B
First-principles calculations of charge transfer in DNA molecules are computationally expensive given that conducting charge carriers interact with intra- and intermolecular atomic motion. Screening sequences, for example, to identify excellent elect...

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Proceedings of the National Academy of Sciences of the United States of America
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological ...

iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.

Analytical biochemistry
An enhancer is a short (50-1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory b...