AIMC Topic: Machine Learning

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Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning.

The Journal of chemical physics
We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers but without the need for subsequent re-optimization w...

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a s...

EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study.

Bioinformatics (Oxford, England)
SUMMARY: In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL m...

Defining the extent of gene function using ROC curvature.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specif...

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.

World journal of gastroenterology
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective tech...

GPU-accelerated approximate kernel method for quantum machine learning.

The Journal of chemical physics
We introduce Quantum Machine Learning (QML)-Lightning, a PyTorch package containing graphics processing unit (GPU)-accelerated approximate kernel models, which can yield trained models within seconds. QML-Lightning includes a cost-efficient GPU imple...

Regimes of ion dynamics in current sheets: The machine learning approach.

Physical review. E
Current sheets are spatially localized almost-one-dimensional (1D) structures with intense plasma currents. They play a key role in storing the magnetic field energy and they separate different plasma populations in planetary magnetospheres, the sola...

Machine-learning-based data-driven discovery of nonlinear phase-field dynamics.

Physical review. E
One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-...

First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be dif...

Artificial intelligence 101 for veterinary diagnostic imaging.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. M...