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Machine learning pinpoints genes that enable plants to grow more with less fertilizer

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Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.


Johns Hopkins has developed a lung cancer blood test

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Powered by artificial intelligence, a new lung cancer blood test developed at Johns Hopkins, combined with other metrics, correctly identified 94% of cancer cases in almost 800 patients. The lung cancer blood test, published in Nature Communications, searches for tiny fragments of DNA released by the tumor cells. The AI looks for patterns in this shattered DNA, rather than looking for specific pieces of cancer DNA like other blood tests in development, New Atlas explained. Lung cancer kills the most people in the world, the authors note, "largely due to the late stage at diagnosis where treatments are less effective than at earlier stages" -- and lung cancer rates are increasing, worldwide. "We believe that a blood test, or'liquid biopsy,' for lung cancer could be a good way to enhance screening efforts, because it would be easy to do, broadly accessible, and cost-effective," study first author Dimitrios Mathios said. The DNA difference: Blood tests for cancer typically focus on finding pieces of mutated tumor DNA.


Artificial Intelligence Uncovers "Genes of Importance" in Agriculture and Medicine

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Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure--which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.


What Is The Real Difference Between Data Engineers and Data Scientists? - KDnuggets

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Although data engineers and data scientists have overlapping skill sets, they fulfill different roles within the fields of big data and AI system development. Data scientists develop analytical models, while data engineers deploy those models in production. As such, data scientists focus primarily on analytics, and data engineers focus more heavily on programming. To launch your data career, you'll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard's Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship.


This AI Could Predict Startup Success with 90% Accuracy, Study Claims

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AI or artificial intelligence could predict startup success to an impressive 90% accuracy, a study using machine learning models that look into tons of companies showed. As per Embroker, startups turn out to be a complete failure in most cases. To be precise, about 90% of them do not become successful. What's more, about 10% of startups end up being a failure every year, regardless of what industry it is in--whether it is from tech or retail. Not to mention that failure began at roughly the second to the fifth year of the firm. However, CBInsights learned in its recent data that 42% of the unsuccessful startups are due to misreading the market demand.


AI For Cancer Detection: Ready for Prime Time or Caution Advised?

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Over the last couple of years, there has been much discussion about the benefits of artificial intelligence (AI) for improving healthcare. But how much of this is true and how much simply hype? Is the technology really a godsend to radiologists and other healthcare professionals, or is it making their lives more difficult? There is no doubt that AI-based image recognition technology has improved enormously in recent years. Many researchers and companies are now working on different types of programs with a view to improving speed, accuracy and costs of cancer screening.


Machine learning helps scientists pinpoint 'genes of importance' in agriculture and medicine - Mountain Top Media

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In a new breakthrough, researchers in the United States and Taiwan have developed a machine learning algorithm to more efficiently identify "genes of importance" in agriculture and medicine.


Machine learning uncovers 'genes of importance' in agriculture and medicine – Phys.org

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Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature …


Peering into the Moon's shadows with AI

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The Moon’s polar regions are home to craters and other depressions that never receive sunlight. Today, a group of researchers led by the Max Planck Institute for Solar System Research (MPS) in Germany presents the highest-resolution images to date covering 17 such craters in the journal Nature Communications. Craters of this type could contain frozen water, making them attractive targets for future lunar missions, and the researchers focused further on relatively small and accessible craters surrounded by gentle slopes. In fact, three of the craters have turned out to lie within the just-announced mission area of NASA's Volatiles Investigating Polar Exploration Rover (VIPER), which is scheduled to touch down on the Moon in 2023. Imaging the interior of permanently shadowed craters is difficult, and efforts so far have relied on long exposure times resulting in smearing and lower resolution. By taking advantage of reflected sunlight from nearby hills and a novel image processing method, the researchers have now produced images at 1-2 meters per pixel, which is at or very close to the best capability of the cameras.


Stanford Releases Report on the Current State of AI

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Artificial intelligence (AI) has significantly advanced in the past half decade and is making major inroads across many industries and sectors worldwide. Earlier this month, Stanford University released The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. The new Stanford AI100 report is the second in a series following the inaugural AI100 report published five years ago in September 2016. Stanford plans to continue to publish the A1100 report once every five years for a hundred years or longer. "The field of artificial intelligence has made remarkable progress in the past five years and is having real-world impact on people, institutions and culture," the researchers wrote.