Wednesday August 21, 2019

scGen- Artificial Intelligence (AI)-Powered Tool Promises to Reshape the Way We Study Diseases

According to the researchers, scGen is a generative deep learning model that leverages ideas from image

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scGen, Artificial Intelligence, Disesase
The study, published in the journal Nature Methods, shows scGen will help mapping and studying cellular response to diseases and their treatment beyond experimentally available data. Pixabay

Researchers have developed — scGen — an Artificial Intelligence (AI)-powered tool which promises to reshape the way we study diseases and their treatment at cellular level.

The study, published in the journal Nature Methods, shows scGen will help mapping and studying cellular response to diseases and their treatment beyond experimentally available data.

According to the researchers, scGen is a generative deep learning model that leverages ideas from image, sequence and language processing and applies them to model the behaviour of a cell performed on computer or via computer simulation.

The next step for the team will be to improve scGen, make it a fully data-driven formulation, increasing its predictive power to enable the study of combinations of perturbations.

scGen, Artificial Intelligence, Disesase
Researchers have developed — scGen — an Artificial Intelligence (AI)-powered tool which promises to reshape the way we study diseases and their treatment at cellular level. Pixabay

“We can now start optimising scGen to answer more and more complex questions about diseases,” said the researcher Alex Wolf from the Technical University of Munich in Germany.

Large-scale atlases of organs in a healthy state are soon going to be available, in particular, within the Human Cell Atlas. This is a significant step in understanding cells, tissues and organs in healthy state in a better way and providing a reference while diagnosing, monitoring and treating diseases.

Accurately modelling cellular response to perturbations e.g. disease, compounds and genetic interventions, is a central goal of computational biology.

In addition, scGen is the first tool that predicts cellular response out-of-sample. This means that scGen, if trained on data that captures the effect of perturbations for a given system, is able to make reliable predictions for a different system.

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“For the first time, we have the opportunity to use data generated in one model system such as mouse and use the data to predict disease or therapy response in human patients,” said Mohammad Lotfollahi from Technical University of Munich. (IANS)

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Artificial Intelligence (AI) can Detect Signs of Irregular Heart Rhythm

The research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening

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Artificial Intelligence, Heart, Rhythm
The AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options. Pixabay

Artificial Intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an electrocardiogram (EKG), even if the heart is in normal rhythm at the time of a test, says new Mayo Clinic research.

The AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options.

The research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening, said the study published in The Lancet.

While common, atrial fibrillation often is fleeting and is challenging to diagnose.

Artificial Intelligence, Heart, Rhythm
Artificial Intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an electrocardiogram (EKG), even if the heart is in normal rhythm at the time of a test, says new Mayo Clinic research. Pixabay

“When people come in with a stroke, we really want to know if they had atrial fibrillation in the days before the stroke, because it guides the treatment,” said Paul Friedman, Chair of the Department of Cardiovascular Medicine at Mayo Clinic.

Blood thinners are very effective for preventing another stroke in people with atrial fibrillation.

“For those without atrial fibrillation, using blood thinners increases the risk of bleeding without substantial benefit. That’s important knowledge. We want to know if a patient has AF,” said Friedman.

Researchers tested AI on normal-rhythm EKGs from a group of 36,280 patients, of whom 3,051 were known to have atrial fibrillation. The AI-enabled EKG correctly identified the subtle patterns of atrial fibrillation with 90 per cent accuracy.

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If proven out, AI-guided EKGs could direct the right treatment for disease caused by atrial fibrillation, even without symptoms.

Moreover, this technology can be processed using a smartphone or watch, making it readily available on a large scale. (IANS)