AIMC Topic: Machine Learning

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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....

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

PloS one
Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity ...

A simple parametric representation of the Hodgkin-Huxley model.

PloS one
The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane ...

Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography.

Abdominal radiology (New York)
PURPOSE: In this study, we developed radiomic models that utilize a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from normal endometrium on routine computed tomography (CT).

Machine learning models as an alternative to determine productivity losses caused by weeds.

Pest management science
BACKGROUND: Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models h...

iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method.

Journal of bioinformatics and computational biology
Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational m...

Cross-Gram matrices and their use in transfer learning: Application to automatic REM detection using heart rate.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years t...

Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cancer science
Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentia...

A Multitask Approach to Learn Molecular Properties.

Journal of chemical information and modeling
The endeavors to pursue a robust multitask model to resolve intertask correlations have lasted for many years. A multitask deep neural network, as the most widely used multitask framework, however, experiences several issues such as inconsistent perf...

Machine learning-based preoperative datamining can predict the therapeutic outcome of sleep surgery in OSA subjects.

Scientific reports
Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep apnea (OSA) and the prediction of whether sleep surgery will have successful outcome is very importa...