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Unparalleled inventory of the human gut ecosystem -- ScienceDaily

"Last year, three independent teams, including ours, reconstructed thousands of gut microbiome genomes. The big questions were whether these teams had comparable results, and whether we could pool them into a comprehensive inventory," says Rob Finn, Team Leader at EMBL-EBI. The scientists have now compiled 200,000 genomes and 170 million protein sequences from more than 4 600 bacterial species in the human gut. Their new databases, the Unified Human Gastrointestinal Genome collection and the Unified Gastrointestinal Protein catalogue, reveal the tremendous diversity in our guts and pave the way for further microbiome research. "This immense catalogue is a landmark in microbiome research, and will be an invaluable resource for scientists to start studying and hopefully understanding the role of each bacterial species in the human gut ecosystem," explains Nicola Segata, Principal Investigator at the University of Trento. The project revealed that more than 70% of the detected bacterial species had never been cultured in the lab -- their activity in the body remains unknown. The largest group of bacteria that falls into that category is the Comantemales, an order of gut bacteria first described in 2019 in a study led by the Bork Group at EMBL Heidelberg. "It was a real surprise to see how widespread the Comantemales are. This highlights how little we know about the bacteria in our gut," explains Alexandre Almeida, EMBL-EBI/Sanger Postdoctoral Fellow in the Finn Team. "We hope our catalogue will help bioinformaticians and microbiologists bridge that knowledge gap in the coming years." A freely accessible data resource All the data collected in the Unified Human Gastrointestinal Genome collection and the Unified Human Gastrointestinal Protein catalogue are freely available in MGnify, an EMBL-EBI online resource that allows scientists to analyse their microbial genomic data and make comparisons with existing datasets.
#paleotherapy #microbiome #AI #gut #machine-learning - sciencedaily.com

Opinion: Just what the doctor ordered: How AI will change medicine in the 2020s - The Globe and Mail

For decades, there has been a steady erosion of the practice of medicine, with progressively less time between patients and doctors, a global epidemic of physician burnout that has now reached a crisis, a doubling of medical errors when doctors have symptoms of depression and most serious errors attributable to bad clinical judgment. Concurrently, each patient’s cumulative data, such as prior history, laboratory tests, scans and sensor output, keeps growing, as has the doctor’s relegation to the role of data clerk. The limited time to think has led one leading physician to conclude: “Modern medical practice is a Petri dish for medical error, patient harm and physician burnout."
#AI #machine-learning - theglobeandmail.com

Announcing ICD-10-CM and RxNorm Ontology Linking for Amazon Comprehend Medical

Medical ontologies, such as ICD-10, make it possible to classify unstructured medical information into standardized codes that downstream healthcare applications, such as revenue cycle management tools (medical coding) can read. Amazon Comprehend Medical ICD-10-CM RXNorm Ontology Linking extracts medical condition and medication entities from medical text and links them to the relevant ICD-10-CM and RXNorm concepts respectively.   Using Amazon Comprehend Medical ICD-10-CM and RXNorm Ontology Linking APIs, developers can quickly and accurately extract codes (e.g. “R51” as the ICD-10-CM code for headache) from a variety of data sources, such as doctor’s notes or patient health records. Our deep learning approach to ontology linking provides much higher accuracy than existing rules-based systems by understanding the context each entity is found in.
#medicalindustrialcomplex #amazon #machine-learning #billing - aws.amazon.com

Real-time personalization and recommendation | Amazon Personalize | AWS

Delivering personalization to individuals at scale requires a combination of the right data and the right technology. The algorithms used by Amazon Personalize are designed to overcome common problems when creating custom recommendations – such as new users with no data, popularity biases, and evolving intent of users – to deliver high-quality recommendations that respond to specific needs, preferences, and behavior of your users.
#amazon #whichworksbest #machine-learning - aws.amazon.com