AIMC Topic: Machine Learning

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Uncertainty-driven dynamics for active learning of interatomic potentials.

Nature computational science
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, th...

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

PLoS computational biology
Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables...

Research on Supply Chain Financial Risk Prevention Based on Machine Learning.

Computational intelligence and neuroscience
Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively u...

Artificial intelligence and machine learning for smart bioprocesses.

Bioresource technology
In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable at...

From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring.

International journal of environmental research and public health
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both...

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress.

Seminars in diagnostic pathology
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of acc...

Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism.

Clinical biochemistry
OBJECTIVE: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried bloo...

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast les...

Artificial intelligence in uveitis: A comprehensive review.

Survey of ophthalmology
Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in health care, their appl...

Machine learning-aided scoring of synthesis difficulties for designer chromosomes.

Science China. Life sciences
Designer chromosomes are artificially synthesized chromosomes. Nowadays, these chromosomes have numerous applications ranging from medical research to the development of biofuels. However, some chromosome fragments can interfere with the chemical syn...