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Machine learning helps grow artificial organs

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Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. In multicellular organisms, the cells making up different organs and tissues are not the same. They have distinct functions and properties, acquired in the course of development. They start out the same, as so-called stem cells, which have the potential to become any kind of cell the mature organism incorporates.


Machine learning will help to grow artificial organs – IAM Network

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Akbar Solo Researchers in Moscow and America have discovered how to use machine learning to grow artificial organs, especially to tackle blindness Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. How would this enable easier organ growth? This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindnessIn multicellular organisms, the cells making up different organs and tissues are not the same.


Machine learning helps grow artificial organs

#artificialintelligence

IMAGE: Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of... view more Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. In multicellular organisms, the cells making up different organs and tissues are not the same. They have distinct functions and properties, acquired in the course of development. They start out the same, as so-called stem cells, which have the potential to become any kind of cell the mature organism incorporates.


How AI is revolutionising drug industry by cutting research time

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Insilico Medicine, named one of the world's top 20 artificial intelligence (AI) drug-development companies by Forbes Magazine, will move its headquarters from the United States to Hong Kong in April. The move from Baltimore to Hong Kong Science Park signals the importance the company places on the China market. Founded by Alex Zhavoronkov in 2014, the enterprise uses AI and deep learning – a subset of machine learning that imitates the workings of the human brain in processing data – for drug discovery and ageing research. Zhavoronkov's credentials are impressive: he has a master's degree in biotechnology from Johns Hopkins University in Baltimore, a physics doctorate from Lomonosov Moscow State University, and is an adjunct professor at the Buck Institute for Research on Ageing in California. Zhavoronkov says AI can speed up and reduce costs for drug development which – involving several phases of clinical trials, government approval and licensing – can last more than a decade.


AI technology can predict your age by gathering physical activity data from smartphones and wearables- Technology News, Firstpost

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Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests. According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields. "Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records," said co-author Peter Fedichev, Science Director at GERO. "Inspired by these examples, we explored AI potential for'Health Risks Assessment' based on human physical activity," Fedichev added. For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES). They trained neural network to predict biological age and mortality risk of the participants from one-week long stream of activity measurements.


Scientists use AI to predict biological age based on smartphone and wearables data

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Researches at longevity biotech company GERO and Moscow Institute of Physics and Technology have developed a computer algorithm that uses Artificial Intelligence to predict biological age and the risk of mortality based on physical activity. The paper is published in Scientific Reports.


Scientists use AI to predict biological age based on smartphone and wearables data

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IMAGE: This is a screenshot of the Gero Lifespan app. Moscow, March 29, 2018 - Researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT) have shown that physical activity data acquired from wearables can be used to produce digital biomarkers of aging and frailty. Many physiological parameters demonstrate tight correlations with age. Various biomarkers of age, such as DNA methylation, gene expression or circulating blood factor levels could be used to build accurate «biological clocks» to obtain individual biological age and the rate of aging estimations. Yet large-scale biochemical or genomic profiling is still logistically difficult and expensive for any practical applications beyond academic research.


The AI that could uncover the secret of eternal youth

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Scientists have revealed a new plan to find the key to eternal youth – and artificial intelligence will be leading the way. Using computer simulations to screen hundreds of compounds, researchers have developed a tool that can identify geroprotectors, the substances responsible for extending healthy life. GeroScope can compare changes in the cells of young and old patients and search for the drugs that counteract the processes. The project is led by scientists from Moscow Institute of Physics and Technology and Insilico Medicine Inc, commissioned by the Center for Biogerontology and Regenerative Medicine. According to the researchers, using computer modelling techniques can help to cut down time and cost in the development of age-combating drugs.


The Unseen

The New Yorker

Once a year, when Slava Epstein was growing up in Moscow, his mother took him to the Exhibition of the Achievements of the National Economy, a showcase for the wonders of Soviet life. The expo featured many things--from industrial harvesters to Uzbek wine--but Epstein, who began going in the nineteen-sixties, when he was eight or nine, was interested primarily in one: the Cosmos Pavilion, a building the size of a hangar, with a ceiling shaped like a giant inverted parabola. Space fever was running high in the city. Since 1961, when Yuri Gagarin orbited the globe, unmanned vessels had been launched toward Mars and Venus. Beside the expo's entrance, the towering Monument to the Conquerors of Space depicted a probe swooping up to the heavens. The Pavilion displayed futuristic technology--Vostok rockets and Soyuz orbiters--but Epstein was less interested in the glories of advanced thruster design than in the glories of space. He wanted to devote himself to astronomy. When a textbook that he found on the topic began with algebraic formulas, he prodded his older brother to explain them. During high school, he enrolled in classes in physics and math at Moscow State University. His parents disapproved of his desired career: because he is half Jewish, Epstein would face harsh Soviet quotas limiting Jews in the study of physics, a field deemed relevant to national security. But after his first lecture the professor invited him for a walk, and affirmed what they had been saying all along. "Don't do it," he warned. Soviet Russia may have been a fatalist's paradise, but from a young age Epstein felt that he was hardwired for optimism. He convinced himself that what is truly important in science is the ability to connect ideas, no matter the field, and so he took up biology. Rather than telescopes, he would use microscopes, which he began taking with him on trips to the White Sea, near the Arctic Circle, to study protozoa along the shore--research that could be conducted with minimal state interference. Over time, he grew interested in even smaller, more ancient forms of life: bacteria. Studying microbes inevitably causes a reordering of one's perceptions: for more than two billion years, they were the only life on this planet, and they remain in many ways its dominant life form. To a remarkable extent, the microbial cosmos was less explored than the actual cosmos: precisely how the organisms evolve, replicate, fight, and communicate remains unclear. Nearly all of microbiology, Epstein eventually learned, was built on the study of a tiny fraction of microbial life, perhaps less than one per cent, because most bacteria could not be grown in a laboratory culture, the primary means of analyzing them. By the time he matured as a scientist, many researchers had given up trying to cultivate new species, writing off the majority as "dark matter"--a term used in astronomy for an inscrutable substance that may make up most of the universe but cannot be seen.