AI Medical Compendium Topic

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

RNA

Showing 141 to 150 of 321 articles

Clear Filters

adabmDCA: adaptive Boltzmann machine learning for biological sequences.

BMC bioinformatics
BACKGROUND: Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for resi...

Identifying N7-methylguanosine sites by integrating multiple features.

Biopolymers
Recent studies reported that N7-methylguanosine (m7G) plays a vital role in gene expression regulation. As a consequence, determining the distribution of m7G is a crucial step towards further understanding its biological functions. Although biologica...

Rilpivirine plus cobicistat-boosted darunavir as alternative to standard three-drug therapy in HIV-infected, virologically suppressed subjects: Final results of the PROBE 2 trial.

Antiviral therapy
BACKGROUND: Primary analysis at 24 weeks showed that switching to rilpivirine plus darunavir/cobicistat was non-inferior to continuing a standard three-drug antiretroviral regimen in virologically suppressed people with HIV. We present efficacy and s...

EditPredict: Prediction of RNA editable sites with convolutional neural network.

Genomics
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to pr...

RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.

Functional & integrative genomics
Adenosine to inosine (A-to-I) editing in RNA is involved in various biological processes like gene expression, alternative splicing, and mRNA degradation associated with carcinogenesis and various human diseases. Therefore, accurate identification of...

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

BMC bioinformatics
BACKGROUND: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods...

Review of machine learning methods for RNA secondary structure prediction.

PLoS computational biology
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary struc...

Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning.

The journal of physical chemistry. B
Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of t...

Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search.

Molecules (Basel, Switzerland)
RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem....

WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction.

Methods (San Diego, Calif.)
The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orch...