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Riboswitch

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Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

Briefings in functional genomics
Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of ...

Tuning the Performance of Synthetic Riboswitches using Machine Learning.

ACS synthetic biology
Riboswitch development for clinical, technological, and synthetic biology applications constantly seeks to optimize regulatory behavior. Here, we present a machine learning approach to improve the regulation of a tetracycline (tc)-dependent riboswitc...

A novel riboswitch classification based on imbalanced sequences achieved by machine learning.

PLoS computational biology
Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the recor...

Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....

A deep learning approach to programmable RNA switches.

Nature communications
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...

CoCoNet-boosting RNA contact prediction by convolutional neural networks.

Nucleic acids research
Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraint...

Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning.

PloS one
Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and ...

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

Methods in molecular biology (Clifton, N.J.)
In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies in...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

Nature communications
RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale w...