Open Genome Informatics

Mentors: Student: Mohamed Amine Haouas

Cancer patients navigating clinical trial databases face complex search interfaces with dozens of technical filters, making it difficult to find relevant trials without medical expertise. This project builds a patient-facing clinical trial chatbot with an interactive map that removes this barrier through natural language. The system features a three-panel interface: a chat panel powered by an LLM agent that elicits patient information and matches it against the Cancer Trials Canada database, an interactive map that displays geocoded trial site locations and a summary panel that shows trial details when a pin is clicked Built using Chainlit server mounted inside FastAPI, React application with Mapbox GL for the interactive map. Trials are stored in PostgreSQL with pgvector for semantic and full-text search. An open-source LLM served via Ollama and Pydantic-AI. Multi-layered guardrails prevent off-topic or harmful responses, RAGAS for evaluation and Langfuse for observability and tracing throughout development.

Mentors: Student: M4x

Parents and caregivers increasingly turn to social media for pediatric ENT advice, where misinformation about conditions like ear infections and hearing loss spreads rapidly and without clinical oversight. This project builds a production-ready, open-source AI surveillance system that monitors Reddit, YouTube, TikTok, and Instagram in real time, classifies emerging trends as HARMFUL, CONCERNING, or SAFE using a 6-node LangGraph agentic pipeline grounded in live PubMed evidence retrieval, and surfaces results to clinicians through a Next.js dashboard with SSE alerts and email notifications for high-risk trends. A working 4-node prototype validated on live Reddit and YouTube data is already implemented. The GSoC deliverables extend this foundation with TikTok and Instagram collectors, ASSESS and REPORT nodes, a production Next.js dashboard replacing the existing Streamlit prototype, APScheduler end-to-end integration, Docker Compose deployment, and formal evaluation against a 300-post clinician-labeled reference set targeting precision > 0.85 on HARMFUL and F1 > 0.80 overall.

Mentors: Student: Chryseis Xinyi Liu

MP-BioPath uses rigorous mathematical programming to predict exactly how genetic mutations or drug perturbations cascade through Reactome's biological pathways. The current model needs better integration of tissue-specific realities and address the issue of artificially diluting predictive signals when multiple proteins share similar roles (entity-set dilution). AND While the Reactome team generated an expansion of these logic networks across their entire database, this massive architecture remains unvalidated at scale. This project aims to transform MP-BioPath into a scalable, context-aware predictive engine. I will architect an automated validation pipeline to stress-test the newly generated Reactome-wide logic networks, and then use this infrastructure to systematically benchmark MP-BioPath against traditional, topology-agnostic methods like Gene Set Enrichment Analysis (GSEA). I will build a transcriptomic-weighting module as the context later: by feeding real-world RNA-seq data into the optimization model, this extension will act as "live traffic updates" for the biological pathways, dynamically adjusting node weights based on actual tissue expression to resolve entity-set dilution and enable highly specific predictions.

Mentors: Student: sskrishn

Currently, at the Ontario Institute of Cancer Research, Jenkins is being used to run the release pipelines and this project proposes moving the system into a more automated format – to use Docker containers with a CI/CD-based system. In the standard release process, as project code will be built and pushed to the main branch for each GitHub repository, as a result, multiple dependencies would also be triggered to be built as well. In this way, we will know if a change in a dependency has correctly integrated into the broader code base. A lot of this testing is done manually, so automating this system can make the time frame for making a release much smaller. With a continuous integration system, we will integrate these artifacts with the release ETL (Extract Transform Load) pipelines.

Mentors: Student: ModeEric

This project entails the creation of the MP-BioPath tool into the FI Viz Cytoscape Plugin. Biological pathways model the set of actions that molecules in a biological cell can perform. What the MP-BioPath does is model the activity of certain biological pathways, helping to predict the response of the pathways to external perturbances. The MP-Biopath tool already exists, but has not been made accessible through the FI Viz Cytoscape Plugin, which is an app designed to analyze the pathways involved with diseases such as cancer. My job would be to incorporate the MP-BioPath tool into the FI Viz Cytoscape Plugin and develop customized visualizations for showing the result of MP-BioPath. If I have time remaining, I intend to deliver a baseline Tensorflow model which utilizes the MP-BioPath tool to create predictions for pathway activity.