Problem statement

When 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:
1). 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.
2). 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.
3). 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.

Solution

1) 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). 

2) 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. 

3) 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.

Roadmap

A lot depends on the amount of funding we will be able to raise, but general plans are:

For genetics AI assistant:

Phase 1: Moving to LLAMA3 and other open-source models (Q2 2024):
* Transferring the code from ChatGPT actions to langchain actions (either directly with langchain or through CrewAI agent framework)
* Setting up open-source webintrafece and setting up interactions with open-source models
* Evaluating how it behaves with open-source models and tunning system instructions

Phase 2: Adding knowledge graphs and additional databases (Q3 2024):
* Adding BioCypher knowledge graph integrations (see https://biocypher.org/ )
* Adding DBSNP, opentargets and other genetic databases (unless they are not already part of knowledge graphs)

Phase 3: Agent-base optimization, active testing and improvements based on user-feedback (Q4 2024):
* Agent-base workflows to improve results on specific types of queries
* Improving performance based on user-feedback
* PEFT (parameter efficient fine-tuning) in case if it is needed and if enough funding will be collected

For GenNet:

Phase 1: Integration Preparation (Q2 2024)
Initial deployment and integration of GenNet into Just-DNA-Seq platform
Phase 2: Testing and Optimization (Late Q2 to Early Q3 2024)
* Conducting testing both internally and in a controlled user group.
* Training additional GenNet models based on UK biobank data
* Collecting feedback from initial tests and making necessary adjustments to the model to optimize performance and user experience.
Phase 3: Full-Scale Deployment (Mid to Late Q3 2024):
* Continuous monitoring and user support to handle any issues or updates