{"signature": "0x3f4d2d2406918977988ef802d2bca9bca951a17102fb0b48287f50b27fcdad8f17c4149bbc9bd12fa0652ab4e6fe0f00b7a5183b44280a5efc9bed902e76b38e1b", "application": {"round": "0x7b150594d8d758bcb2c3f855ffab3ece27ad2983", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "SNN0EDonBefiwqXFxVdb/n7UKuaVfqyaD5Ywsk3PmcdudMcQ1jY09xa9M9IBG8rF", "encryptedSymmetricKey": "74e25e9a34abded471189d75d4cf4b14a45adac4c94208c7783d5f7291ca17d73931c48d3e021ac4274afd9189310a02b39ecfd74d0acd9e208be229e463d3cd518f9c4d362425fb3bd0b5b87a268aab020e727d56a8da7c15f59565a1c4c4996c8af50333210f1de6a6e01dd4fcf69832c473eae5b75b3c2aceed58742c2080000000000000002091485ba8e37bcbafc9fa254b49774b062e45d83ba9bec6cbbb37a0ac76e987099b7f002b0091a18b8aca3a3a2d6a6e99"}}, {"type": "short-answer", "answer": "gitcoin", "hidden": false, "question": "Funding Sources", "questionId": 1}, {"type": "number", "answer": "5", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "short-answer", "answer": "antonkulaga", "hidden": true, "question": "Telegram username", "questionId": 3}, {"type": "paragraph", "answer": "70K", "hidden": true, "question": "Total prior funding for project in USD", "questionId": 4}, {"type": "paragraph", "answer": "Maria Koval https://www.linkedin.com/in/marfantyk/ and https://github.com/marfantyk\nAlina Fyodorova https://www.linkedin.com/in/lnalinafedorova/ \nOlga Borysova: https://www.linkedin.com/in/olga-borysova-79390884/\nNewton Winter: https://github.com/winternewt and https://www.linkedin.com/in/nikolai-usanov-853b32264/", "hidden": true, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 5}, {"type": "paragraph", "answer": "38", "hidden": true, "question": "How old is the project? (Months)", "questionId": 6}, {"type": "paragraph", "answer": "Problem statement\n\nWhen you sequence your DNA or that of your loved ones, you're left wondering how your genetic code affects your health, intelligence and other traits. The problem lies in three key areas:\n1). Lack of transparency and trust: Commercial companies often use proprietary algorithms and databases, making it impossible to understand how they arrive at their conclusions. It's like trying to decipher a code without the key. And, to make matters worse, different companies may give you conflicting results.\n2). Lack of explainability: Traditional tools struggle to make sense of the vast amounts of genetic data. New machine learning models can handle this complexity. However, these models often come as 'black boxes'—powerful yet impenetrable, offering little to no insight into which biological processes are involved.\n3). Lack of personal expertise: While you may know a lot about yourself and your relatives, you're not a geneticist. Interpreting the results of your genetic testing requires specialized knowledge. Even when the results are transparent, it's like trying to read a foreign language without a translator.\n\nSolution\n\n1) The solution for the lack of transparency and trust is already deployed. It is our open-source platform ( https://dna-seq.github.io/ ) is available for anyone to use, and you can run it on your laptop instead of our server. You can also check our code and trace how each prediction was made. All the code and databases are open-source and available on our GitHub (https://github.com/dna-seq). \n\n2) The solution for the lack of explainability is already in progress. We've started implementing GenNet ( https://doi.org/10.1038/s42003-021-02622-z  ) module. GenNet is an interpretable neural network model designed for genomic data, into our personalized genomic service. We've also gained access to the UK Biobank, which we plan to use to train new models. \n\n3) The solution for the lack of personal expertise is underway. We've already developed Genetics Genie, a custom GPT that answers genetics questions with knowledge of some genetic databases. If you have ChatGPT Pro account you can try it at https://chat.openai.com/g/g-HnvTUyFeq-genetics-genie , you can also check its code. However, we want to extend its capabilities using open-source LLMs like LLAMA 3 and Mixstral. We also want to increase the number of genetics databases it knows and improve its explanations.\n\nRoadmap\n\nA lot depends on the amount of funding we will be able to raise, but general plans are:\n\nFor genetics AI assistant:\n\nPhase 1: Moving to LLAMA3 and other open-source models (Q2 2024):\n* Transferring the code from ChatGPT actions to langchain actions (either directly with langchain or through CrewAI agent framework)\n* Setting up open-source webintrafece and setting up interactions with open-source models\n* Evaluating how it behaves with open-source models and tunning system instructions\n\nPhase 2: Adding knowledge graphs and additional databases (Q3 2024):\n* Adding BioCypher knowledge graph integrations (see https://biocypher.org/ )\n* Adding DBSNP, opentargets and other genetic databases (unless they are not already part of knowledge graphs)\n\nPhase 3: Agent-base optimization, active testing and improvements based on user-feedback (Q4 2024):\n* Agent-base workflows to improve results on specific types of queries\n* Improving performance based on user-feedback\n* PEFT (parameter efficient fine-tuning) in case if it is needed and if enough funding will be collected\n\nFor GenNet:\n\nPhase 1: Integration Preparation (Q2 2024)\nInitial deployment and integration of GenNet into Just-DNA-Seq platform\nPhase 2: Testing and Optimization (Late Q2 to Early Q3 2024)\n* Conducting testing both internally and in a controlled user group.\n* Training additional GenNet models based on UK biobank data\n* Collecting feedback from initial tests and making necessary adjustments to the model to optimize performance and user experience.\nPhase 3: Full-Scale Deployment (Mid to Late Q3 2024):\n* Continuous monitoring and user support to handle any issues or updates", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 7}, {"type": "paragraph", "answer": "Our project really ties into the DeSci goal of making science accessible to everyone from its very inception. We’ve built it on open-source tech so that anyone can get personalized genomic information easily and freely. This means users can get detailed genetic reports that touch on important health and longevity topics, all in line with DeSci’s values of openness and community collaboration. We're now working on making our tools even more user-friendly by adding explainable AI models and creating a helpful AI assistant. This will help everyone understand their genetics better and make informed health decisions, pushing forward the idea of science by and for the people.", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 8}, {"type": "paragraph", "answer": "⅔ will be used to compensate for effort. ⅓ will be used on IT infrastructure and computation.", "hidden": false, "question": "Describe how funds from the grants program will be used for your project.", "questionId": 9}, {"type": "paragraph", "answer": "0x15a88cC30E245dcEC019312B5F5c817936FA0154", "hidden": true, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta) using a different project payout wallet address, please share it here.", "questionId": 10}, {"type": "paragraph", "answer": "Our project was initially funded by Longevity Round, which was very successful and let us develop multiple source code repositories ( see https://github.com/dna-seq ), publish 11 open-source Oak Var modules, and conduct several hack-your-own-genome workshops.\nThe funding from the DeSci grant round was very modest, but it allowed us to:\n* develop Genetics Genie where we integrated results from the Just-DNA-Seq project with Longevity genie ( https://chat.openai.com/g/g-HnvTUyFeq-genetics-genie with open-source backend at https://github.com/longevity-genie/restful-genie )\n* push our paper draft to a preprint https://arxiv.org/abs/2403.19087 which we are submitting to journals now \n* make several important usability improvements both to OakVar and Just-DNA-Seq app\nLast time, we also planned to integrate Plex into our dna-seq pipelines to make them truly decentralized, but we had to cut it due to insufficient funding, along with some other web3 features.", "hidden": false, "question": "If this project participated in past DeSci grant rounds, please share any new updates or milestones since the last round.", "questionId": 11}, {"type": "paragraph", "answer": "Our preprint https://arxiv.org/abs/2403.19087", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 12}, {"type": "dropdown", "answer": "Yes", "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 13}, {"type": "multiple-choice", "hidden": true, "question": "Please read the following exclusion criteria that are not permitted and will result in rejection or removal from the round.", "questionId": 14}], "project": {"id": "0x42214708589492950b00ddc6ecf98f3c4b0eda1d5dd0f59967dd8761ccc110a4", "title": "Just-DNA-Seq", "logoImg": "bafkreia3ei7wli3sstfcpwgivps4hmdeph6hdwwiio5jqvm765ae3wfuju", "metaPtr": {"pointer": "bafkreiacf4v62xaes6bl5qx24qjrs54pn7iiup2iciasvvqryjmp7hjmy4", "protocol": "undefined"}, "website": "https://dna-seq.github.io", "bannerImg": "bafkreiciyiho4d6xaxfuyo7zwn2xx6dd2332f2rqan3p5obwczmd74uoee", "createdAt": 1681763994346, "userGithub": "antonkulaga", "credentials": {}, "description": "Just DNA-Seq ( http://dna-seq.github.io/ ) is an open-source personalized genomics platform. It is also a suite of open-source libraries and pipelines to empower individuals to take control of their genetic information. Initially funded by GitCoin during the longevity funding round, we have successfully delivered on most of our promises: with 23 source code repositories ( see https://github.com/dna-seq ), 11 published Oak Var modules, several hack-your-own-genome workshops together with documentation and scientific research paper https://arxiv.org/abs/2403.19087 ( preprint ).\n\nWhen you sequence your DNA or that of your loved ones, you're left wondering how your genetic code affects your health, intelligence and other traits. The problem lies in three key areas:\n\nTransparency and trust. \n\nCommercial companies often use proprietary algorithms and databases, making it impossible to understand how they arrive at their conclusions. It's like trying to decipher a code without the key. And, to make matters worse, different companies may give you conflicting results.\n\nThe solution for the lack of transparency and trust is already deployed. It is our open-source platform ( https://dna-seq.github.io/ ) is available for anyone to use, and you can run it on your laptop instead of our server. You can also check our code and trace how each prediction was made. All the code and databases are open-source and available on our GitHub (https://github.com/dna-seq). You can even put your own filters and explore your genome in detail instead of just reading our reports. However, our output is still somewhat technical for non-professionals and we need to improve it and add more tutorials and documentation.\n\nExplainability. \n\nTraditional tools struggle to make sense of the vast amounts of genetic data. It's like trying to find a needle in a haystack. New machine learning models can handle this complexity. However, these models often come as 'black boxes'—mighty yet impenetrable, offering little to no insight into which biological processes are involved.\n\nThe solution for the lack of explainability is already in progress. We've started implementing GenNet (https://doi.org/10.1038/s42003-021-02622-z), an interpretable neural network model designed for genomic data, into our personalized genomic service. We've also gained access to the UK Biobank, which we plan to use to train new models. To continue our efforts, we need your support.\n\nRequirements for personal expertise. \n\nWhile you may know a lot about yourself and your relatives, you're not a geneticist. Interpreting the results of your genetic testing requires specialized knowledge. Even when the results are transparent, it's like trying to read a foreign language without a translator.\n\nThe solution to the lack of personal expertise is underway. We've already developed Genetics Genie, a custom GPT that answers genetics questions with knowledge of some genetic databases. If you have ChatGPT Pro account, you can try it at https://chat.openai.com/g/g-HnvTUyFeq-genetics-genie , you can also check its code. However, we want to extend its capabilities using open-source LLMs like LLAMA 3 and Mixstral. We also want to increase the number of genetics databases it knows and improve its explanations. With your support, we can continue to develop AI assistant that provide clear and concise explanations, making genetic knowledge accessible to everyone.\n\nBy supporting our open-source project, you'll be helping to create a more transparent, explainable, and accessible way to understand the secrets of your genome. Join us in democratizing personalized genomics and empowering individuals to take control of their health.", "lastUpdated": 0, "projectGithub": "dna-seq", "projectTwitter": "just_dna_seq"}, "recipient": "0x0C5B77597A614ff750b754910b8A226639b262C5"}}