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The advances were reported by a collaboration between KTH Royal Institute of Technology, CCP Games and Massively Multiplayer Online Science. In a study published in the September issue of Nature Biotechnology, the researchers found that gamers, or "citizen scientists," helped boost the AI system used for predicting protein localization on a subcellular level. The combination of crowdsourcing and AI led to improved classification of subcellular protein patterns and the first-time identification of 10 new members of the family of cellular structures known as "Rods & Rings," says Emma Lundberg, a researcher from KTH who leads the Cell Atlas, part of the Human Protein Atlas, at the Science for Life joint research center. Lundberg says the data is being actively integrated into the publicly-available Human Protein Atlas database and will be a resource for researchers worldwide who are working towards a greater understanding of human cells, proteins and disease development. The researchers partnered with Massively Multiplayer Online Science and CCP Games to integrate analysis of protein localization from the Human Protein Atlas Cell Atlas images directly into EVE Online, a popular massively multiplayer online game.
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Researchers at Purdue University have developed a new deep learning algorithm, called DOVE, that can improve modelling of proteins and help create new drugs. The human body contains over 20,000 different types of proteins, which interact with each other to enable life as we know it. Currently, protein docking models have been developed to estimate how two proteins will interact, yet it is challenging to score whether or not the predicted docking estimate is correct. The Purdue researchers developed a new computational method to address this challenge. DOVE, short for Docking decoy selection with Voxel-based deep neural nEtwork, first scans protein-protein interfaces of a proposed protein docking configuration using a 3D voxel, while considering the atomic interactions and energetic contributions.
August 20th 2018 – Reykjavik, Iceland – Researchers from KTH Royal Institute of Technology and Massive Multiplayer Online Science (MMOS) worked with CCP Games using their massively multiplayer online game set in space, EVE Online to gain a more granular understanding of patterns of proteins arranged within the body's cells. Built on a map that shows hundreds of thousands of microscopic images of human cells, EVE Online players worked alongside an artificial intelligence to accomplish this goal. In a study to be published in the September issue of Nature Biotechnology, the researchers found that players, or "citizen scientists" as KTH and MMOS now call them, helped boost the artificial intelligence system used for predicting protein localization on a subcellular level. The combination of crowdsourcing and AI led to improved classification of subcellular protein patterns and the first-time identification of ten new members of the family of cellular structures known as "Rods & Rings," according to Emma Lundberg, a researcher from KTH who leads the Cell Atlas, part of the Human Protein Atlas, at the Science for Life joint research center. She is also the first ever scientist who was put into a videogame as an agent NPC (non-playable character) to direct the project in-game as Professor Lundberg.