AIMC Topic: Machine Learning

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Quantum Similarity Testing with Convolutional Neural Networks.

Physical review letters
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Lette...

Machine learning-assisted antenna modelling for realistic assessment of incident power density on non-planar surfaces above 6 GHz.

Radiation protection dosimetry
In this paper, the analysis of exposure reference levels is performed for the case of a half-wavelength dipole antenna positioned in the immediate vicinity of non-planar body parts. The incident power density (IPD) spatially averaged over the spheric...

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.

Briefings in bioinformatics
The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in d...

Machine learning for RNA 2D structure prediction benchmarked on experimental data.

Briefings in bioinformatics
Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were...

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.

Briefings in bioinformatics
CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method...

ENRICHing medical imaging training sets enables more efficient machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In...

FLAN: feature-wise latent additive neural models for biological applications.

Briefings in bioinformatics
MOTIVATION: Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on ...

A review of enzyme design in catalytic stability by artificial intelligence.

Briefings in bioinformatics
The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, Al...

Analysis of super-enhancer using machine learning and its application to medical biology.

Briefings in bioinformatics
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the o...

A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning.

Studies in health technology and informatics
Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effecti...