## Inspiration

## What it does

PhyloForest helps researchers and educators by improving how we see phylogenetic trees. Strong, useful data visualization is key to new discoveries and patterns. Thanks to our product, users have a greater ability to perceive depth of trees by communicating widths rather than lengths. The length between proteins is based on actual lengths scaled to size.

## How we built it

We used EggNOG to get phylogenetic trees in Newick format, then parsed them using a recursive algorithm to get the differences between the protein group in question. We connected names to IDs using the EBI (European Bioinformatics Institute) database, then took the lengths between the proteins and scaled them to size for our Unity environment. After we put together all this information, we went through an extensive integration process with Unity. We used EBI APIs for Taxon information, EggNOG gave us NCBI (National Center for Biotechnology Information) identities and structure. We could not use local NCBI lookup (as eggNOG does) due to the limitations of Virtual Reality headsets, so we used the EBI taxon lookup API instead to make the tree interactive and accurately reflect the taxon information of each species in question. Lastly, we added UI components to make the app easy to use for both educators and researchers.

## Challenges we ran into

Parsing the EggNOG Newick tree was our first challenge because there was limited documentation and data sets were very large. Therefore, it was difficult to debug results, especially with the Unity interface. We also had difficulty finding a database that could connect NCBI IDs to taxon information with our VR headset. We also had to implement a binary tree structure from scratch in C#. Lastly, we had difficulty scaling the orthologs horizontally in VR, in a way that would preserve the true relationships between the species.

## Accomplishments that we're proud of

The user experience is very clean and immersive, allowing anyone to visualize these orthologous groups. Furthermore, we think this occupies a unique space that intersects the fields of VR and genetics. Our features, such as depth and linearized length, would not be as cleanly implemented in a 2-dimensional model.

## What we learned

We learned how to parse Newick trees, how to display a binary tree with branches dependent on certain lengths, and how to create a model that relates large amounts of data on base pair differences in DNA sequences to something that highlights these differences in an innovative way.

## What's next for PhyloForest

Making the UI more intuitive so that anyone would feel comfortable using it. We would also like to display more information when you click on each ortholog in a group. We want to expand the amount of proteins people can select, and we would like to manipulate proteins by dragging branches to better identify patterns between orthologs.
## Inspiration

The inspiration behind LeafHack stems from a shared passion for sustainability and a desire to empower individuals to take control of their food sources. Witnessing the rising grocery costs and the environmental impact of conventional agriculture, we were motivated to create a solution that not only addresses these issues but also lowers the barriers to home gardening, making it accessible to everyone.

## What it does

Our team introduces "LeafHack" an application that leverages computer vision to detect the health of vegetables and plants. The application provides real-time feedback on plant health, allowing homeowners to intervene promptly and nurture a thriving garden. Additionally, the images uploaded can be stored within a database custom to the user. Beyond disease detection, LeafHack is designed to be a user-friendly companion, offering personalized tips and fostering a community of like-minded individuals passionate about sustainable living

## How we built it

LeafHack was built using a combination of cutting-edge technologies. The core of our solution lies in the custom computer vision algorithm, ResNet9, that analyzes images of plants to identify diseases accurately. We utilized machine learning to train the model on an extensive dataset of plant diseases, ensuring robust and reliable detection. The database and backend were built using Django and Sqlite. The user interface was developed with a focus on simplicity and accessibility, utilizing next.js, making it easy for users with varying levels of gardening expertise

## Challenges we ran into

We encountered several challenges that tested our skills and determination. Fine-tuning the machine learning model to achieve high accuracy in disease detection posed a significant hurdle as there was a huge time constraint. Additionally, integrating the backend and front end required careful consideration. The image upload was a major hurdle as there were multiple issues with downloading and opening the image to predict with. Overcoming these challenges involved collaboration, creative problem-solving, and continuous iteration to refine our solution.

## Accomplishments that we're proud of

We are proud to have created a solution that not only addresses the immediate concerns of rising grocery costs and environmental impact but also significantly reduces the barriers to home gardening. Achieving a high level of accuracy in disease detection, creating an intuitive user interface, and fostering a sense of community around sustainable living are accomplishments that resonate deeply with our mission.

## What we learned

Throughout the development of LeafHack, we learned the importance of interdisciplinary collaboration. Bringing together our skills, we learned and expanded our knowledge in computer vision, machine learning, and user experience design to create a holistic solution. We also gained insights into the challenges individuals face when starting their gardens, shaping our approach towards inclusivity and education in the gardening process.

## What's next for LeafHack

We plan to expand LeafHack's capabilities by incorporating more plant species and diseases into our database. Collaborating with agricultural experts and organizations, we aim to enhance the application's recommendations for personalized gardening care.
## Inspiration

Our inspiration came from the challenge proposed by Varient, a data aggregation platform that connects people with similar mutations together to help doctors and users.

## What it does

Our application works by allowing the user to upload an image file. The image is then sent to Google Cloud’s Document AI to extract the body of text, process it, and then matched it against the datastore of gene names for matches.

## How we built it

While originally we had planned to feed this body of text to a Vertex AI ML for entity extraction, the trained model was not accurate due to a small dataset. Additionally, we attempted to build a BigQuery ML model but struggled to format the data in the target column as required. Due to time constraints, we explored a different path and downloaded a list of gene symbols from HUGO Gene Nomenclature Committee’s website (<https://www.genenames.org/>). Using Node.js, and Multer, the image is processed and the text contents are efficiently matched against the datastore of gene names for matches. The app returns a JSON of the matching strings in order of highest frequency. This web app is then hosted on Google Cloud through App Engine at (<https://uofthacksix-chamomile.ue.r.appspot.com/>).

The UI while very simple is easy to use. The intent of this project was to create something that could easily be integrated into Varient’s architecture. Converting this project into an API to pass the JSON to the client would be very simple.

## How it meets the theme "restoration"

The overall goal of this application, which has been partially implemented, was to create an application that could match mutated gene names from user-uploaded documents and connect them with resources, and others who share the common gene mutation. This would allow them to share strategies or items that have helped them deal with living with the gene mutation. This would allow these individuals to restore some normalcy in their lives again.

## Challenges we ran into

Some of the challenges we faced:

* having a small data set to train the Vertex AI on
* time constraints on learning the new technologies, and the best way to effectively use it
* formatting the data in the target column when attempting to build a BigQuery ML model

## Accomplishments that we're proud of

The accomplishment that we are all proud of is the exposure we gained to all the new technologies we discovered and used this weekend. We had no idea how many AI tools Google offers. The exposure to new technologies and taking the risk to step out of our comfort zone and attempt to learn and use it this weekend in such a short amount of time is something we are all proud of.

## What we learned

This entire project was new to all of us. We have never used google cloud in this manner before, only for Firestore. We were unfamiliar with using Express and working with machine learning was something only one of us had a small amount of experience with. We learned a lot about Google Cloud and how to access the API through Python and Node.js.

## What's next for Chamomile

The hack is not as complete as we would like since ideally there would be a machine learning aspect to confirm the guesses made by the substring matching and more data to improve the Vertex AI model. Improving on this would be a great step for this project. Also, add a more put-together UI to match the theme of this application.