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</h5><span class="label label-info">September 2016 - August 2018</span>
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Stack used: <br>Angular4, Node.js, Express, MongoDB, RESTful APIs, RAML, Git with CICD and Python<br>
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Development of human computation game "Phylo" and "Ribo" using the concepts of Human computation, Machine learning, Bio-informatics and Web development.<br>
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Stack used: <br>Python, Angular4, Node.js, Express, MongoDB, RESTful APIs, RAML, Git, automated testing via Comtinuous Integration using Mocha, chai and Selenium.<br>
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I developed the backend of the game Phylo. Essentially, I developed RESTful APIs for machine learning components like: difficulty prediction of puzzles, extraction of interesting puzzles from the multiple DNA/RNA sequences (MSA) using CNN models, routing of puzzles and dynamic change in difficulty of puzzles in the game. Extraction of segments of MSA means identifying regions that are not perfectly aligned via machine. Via Phylo, we managed to achieve this with an accuracy of 84.6% which is 9% more than the existing state-of-art. Stack used for this includes: Nodejs, Python, and MongoDB. The game is now deployed in Science and Technology Museum, Ottawa. This is a game with a purpose and is used to solve the sequence alignment problem of DNA/RNA. Complete end-to-end development of OpenPhylo is done by me.<br>
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