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genetic disease


Using Machine Learning to Detect Epilepsy in Children - DZone Big Data

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Artificial Intelligence has been making impressive strides in the past year or so, with a number of medical applications utilizing AI to spot problems in medical imagery more effectively and efficiently than current methods. For instance, we've had a couple of projects using the approach to better identify cancer, eye problems, and liver disease. A recent study has set out to do a similar feat to help researchers detect epilepsy in children. The research, which was a collaborative project between Young Epilepsy, UCL Great Ormond Street Institute of Child Health and the University of Cambridge, focused on Focal Cortical Dysplasia, which is a major cause of epilepsy in children. It describes the way the brain fails to form normally, and because the abnormalities tend to be small, they tend to be very difficult to pick up on MRI scans.


Pharma startup Quris aims to use a 'patient on a chip' to target drug delivery

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Nobel Laureate, Aaron Ciechanover, is one of several notable names behind pharma startup Quris. The company aims to bring together artificial intelligence, the industry's vast knowledge of the human genome, and the concept of the "patient on a chip" to improve the effectiveness of drug delivery. Last month, the startup announced the launch of its AI platform and a $9 million seed round, led by Moshe Yanai (the mind behind EMC Symmetrix) and Dr. Judith Richter, and Dr. Jacob Richter (founders of Medinol, which has sold more than 2 million cardiovascular stents). Ciechanover, as well as Moderna cofounder, Robert Langer, are among Quris' noteworthy advisers. For decades, medical research has successfully cured cancer and treated rare diseases in innumerable quantities of mice – but has not done so as frequently in humans.


Utah doctors using artificial intelligence to identify rare genetic diseases in babies

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While many babies are born without issues on a daily basis, there are quite a few who are born early and with some complications. Those babies end up in the Neonatal Intensive Care Unit (NICU), which is where Dr. Sabrina Malone Jenkins works. The Neonatologist at University of Utah Health and Intermountain Primary Children's Hospital said, "A lot of the time we don't know what the underlying condition is and there is a wide variety of causes and rarely are any of them the same." Researchers say on a global scale about seven million infants are born with serious genetic disorders each year and it can be tough for doctors to treat them if they're not sure what's wrong. Dr. Mark Yandell, a Professor in the Department of Human Genetics at the University of Utah said, "It's estimated that about 20% of the newborns in a high-intensity newborn intensive care unit have some form of genetic disease."


Utah doctors using artificial intelligence to identify rare genetic diseases in babies – Fox 13

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Artificial Intelligence (AI) is helping doctors diagnose rare diseases in babies and Utah doctors are using the tool with the hope of saving more …


Israeli team says AI platform can predict which drugs are safe

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Robert Langer, the co-founder of Moderna and a lauded MIT professor, said, "We are at the tipping point of the modernization of drug discovery" and that the "Quris platform could be a significant value to pharma companies and the health of society at large." Langer is a member of the scientific advisory board of Quris, which officially launched this week and announced $9 million in seed funding to support its efforts. Nobel laureate Aaron Ciechanover is the chairman of the company's scientific advisory board. Quris, based in Israel and Boston, is an artificial intelligence (AI) company operating in the pharmaceutical space. Its team has developed an AI platform to predict which drug candidates will work most safely and effectively in humans.


Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases - Genome Medicine

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Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.



La veille de la cybersécurité

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Despite being frequent, this disorder is quite challenging to diagnose before the age of five. Scientists at the University of Geneva now have come up with an AI algorithm based on the automated analysis of videos, making it possible to study children's non-verbal communication in an anonymous and standardized manner. Nada Kojovic, a researcher in Marie Schaer's team and first author of the study, said, "Autism is characterized by a non-verbal communication that differs from that of a typically- developing child. It differs on several points, such as the difficulty in establishing eye contact, smiling, pointing to objects or the way they are interested in what surrounds them." "This is why we designed an algorithm using artificial intelligence that analyses the children's movements on video and identifies whether or not they are characteristic of autism spectrum disorder."


Using AI, scientists developed a device to detect autism

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Despite being frequent, this disorder is quite challenging to diagnose before the age of five. Scientists at the University of Geneva now have come up with an AI algorithm based on the automated analysis of videos, making it possible to study children's non-verbal communication in an anonymous and standardized manner. Nada Kojovic, a researcher in Marie Schaer's team and first author of the study, said, "Autism is characterized by a non-verbal communication that differs from that of a typically- developing child. It differs on several points, such as the difficulty in establishing eye contact, smiling, pointing to objects or the way they are interested in what surrounds them." "This is why we designed an algorithm using artificial intelligence that analyses the children's movements on video and identifies whether or not they are characteristic of autism spectrum disorder."


The Poop About Your Gut Health and Personalized Nutrition

WIRED

Changing your diet to improve your health is nothing new--people with diabetes, obesity, Crohn's disease, celiac disease, food allergies, and a host of other conditions have long done so as part of their treatment. But new and sophisticated knowledge about biochemistry, nutrition, and artificial intelligence has given people more tools to figure out what to eat for good health, leading to a boom in the field of personalized nutrition. Personalized nutrition, often used interchangeably with the terms "precision nutrition" or "individualized nutrition" is an emerging branch of science that uses machine learning and "omics" technologies (genomics, proteomics, and metabolomics) to analyze what people eat and predict how they respond to it. Scientists, nutritionists, and health care professionals take the data, analyze it, and use it for a variety of purposes including identifying diet and lifestyle interventions to treat disease, promote health, and enhance performance in elite athletes. Increasingly, it's being adopted by businesses to sell products and services such as nutritional supplements, apps that use machine learning to provide a nutritional analysis of a meal based on a photograph, and stool-sample tests whose results are used to create customized dietary advice that promises to fight bloat, brain fog, and a myriad of other maladies.