GSOC Project Ideas 2026
Got an idea for GSOC 2026?’
Then please post it. You can either
- Add it here, by directly editing this page. Just copy, paste and
update the template below. This requires that you create
a fork of this repo and then make a pull request with the changes.
Projects can use a broad set of skills, technologies, and domains, such
as GUIs, database integration and algorithms.
Students are also encouraged to propose their own ideas related to our
projects. If you have strong computer skills and have an interest in
biology or bioinformatics, you should definitely apply! Do not hesitate
to propose your own project idea: some of the best applications we see
are by students who go this route. As long as it is relevant to one of
our projects, we will give it serious consideration. Creativity and
self-motivation are great traits for open-source programmers.
Proposed project ideas for 2026
MP-BioPath Perturation Analysis / Stein Lab
- Brief explanation: MP-BioPath is a computational tool designed to
predict the effects of perturbations on biological pathways. Utilizing
Reactome’s pathway models, MP-BioPath employs an optimization model.
Our objective is to develop pipelines and tools that integrate
MP-BioPath results with genomic data.
- Expected results: As a result of this project, we aim to develop
tools and pipelines capable of handling diverse genomic datasets.
Additionally, we anticipate the generation of novel biologically
significant insights.
- Project Home Page URL:
Reactome
MP-BioPath
- Project paper reference and URL:
“Evaluating the predictive accuracy
of curated biological pathways in a public knowledgebase”
- Knowledge prerequisites: Python, R, Julia
- Skill level: Medium
- Project Time: 175-hour approximately 8 weeks
- Mentors: Adam Wright <adam.wright@oicr.on.ca>
Patient-Facing Clinical Trial Chatbot with Interactive Map / Stein and Pai Labs
- Brief explanation: Cancer patients are often left on their own to find clinical trials of cutting-edge therapies. This project seeks to develop an LLM-driven chatbot and interactive map that lets patients describe their situation and find nearby clinical trial sites that they may be eligible for.
- Expected results: As a result of this project, patients will be able to more effectively discover clinical trials, learn more about them, and contact the study doctors to seek enrollment.
- Project Home Page URL: There is no project web page at the moment, but you can get an idea of the type of underlying database we will be using at the Cancer Trials Canada website.
- Knowledge prerequisites: SQL, Python, React (TypeScript), familiarity with Chainlit (LLM) and Mapbox (Geomapping) APIs
- Skill level: Medium
- Project Time: 175-hour approximately 8 weeks
- Mentors: Lincoln Stein <lincoln.stein@gmail.com>, Shraddha Pai <spai@oicr.on.ca>.
Pathway Reasoning with LLM / Stein Lab
- Brief explanation: Reactome houses a meticulously curated repository
of human biological pathways. Our current initiative focuses on
crafting a RAG chat application optimized for intuitive interaction
with the Reactome web portal. Our primary aim is to empower the
application to interpret user queries and leverage the LLM (Language
Model) to delve deep into pathway structures, enabling the generation
of comprehensive and insightful responses for users.
- Expected results: expected outcomes include the application’s
ability to effectively handle a diverse range of user queries and to
expand its capabilities to accommodate an increased number of use
cases. Furthermore, the application is expected to leverage advanced
reasoning capabilities powered by the LLM, thereby providing more
insightful and comprehensive responses tailored to each user’s inquiry
- Project Home Page URL:
Reactome
- Project paper reference and URL:
- Knowledge prerequisites: Python, RAG
- Skill level: Medium
- Project Time: 175-hour approximately 8 weeks
- Mentors: Adam Wright <adam.wright@oicr.on.ca>
Reactome MCP / Stein Lab
- Brief explanation: Reactome provides users with various computational
interfaces for computationally accessing the curated biological pathways,
including analysis tools and a chat interface React-to-Me. A Reactome MCP would
make the website more computationally accessible by providing access to the tools
through React-to-Me and other LLM based chat interfaces.
- Expected results: expected outcomes include the application’s
ability to run Reactome analysis tools through the React-to-Me chat interface.
Other features of Reactome, including our REST APIs, should be made accessible
to LLMs through the MCP.
- Project Home Page URL:
React-to-Me
- Project paper reference and URL:
- Knowledge prerequisites: Python, RAG, MCP
- Skill level: Medium
- Project Time: 175-hour approximately 8 weeks
- Mentors: Adam Wright <adam.wright@oicr.on.ca>
- Brief explanation: This project will develop and validate a real-time artificial intelligence (AI) application that continuously monitors major social media platforms (TikTok, YouTube, Instagram, X, and Reddit) to identify emerging health-related trends involving ear, nose, and throat (ENT) issues among children and adolescents. The system will classify and rank viral behaviors based on engagement metrics and notify pediatric otolaryngologists about potentially harmful trends or misinformation. The goal is to explore how automated social media surveillance can support early awareness and clinical decision-making in pediatric otolaryngology.
- Expected results: expected outcomes include:
- A working prototype of a real-time social media monitoring pipeline.
- Automated collection of public data via platform APIs.
- NLP/LLM-based classification and ranking of pediatric ENT-related trends.
- A reporting dashboard visualizing trends, engagement metrics, and risk flags for clinicians.
Evaluation of model performance (precision, recall, accuracy) and preliminary assessment of clinical usefulness with pediatric otolaryngologists.
Documentation and open-source code suitable for further research and extension.
- Project Home Page URL:
Host lab webpage, no specific project page yet
- Project paper reference and URL: No existing paper yet; this project will contribute to future publications on AI-driven social media surveillance in pediatric otolaryngology.
- Knowledge prerequisites: Programming languages: Python (for AI/NLP and data pipelines), JavaScript/TypeScript (for frontend)
Experience with:
- REST APIs and social media data extraction
- NLP and/or LLM integration
- Basic machine learning workflows
- Full-stack development (backend services + frontend dashboards)
- Skill level: Advanced
- Project Time: 350-hour approximately 8 weeks
- Mentors: Melanie Courtot, OICR and UoT <mcourtot@oicr.on.ca>; Jochen Weile, OICR, <jweile@oicr.on.ca>
Project template
Template: Project Idea Name (Project Name/Lab Name)
- Brief explanation: Brief description of the idea, including any
relevant links, etc.
- Expected results: describe the outcome of the project idea.
- Project Home Page URL: if there is one.
- Project paper reference and URL: Is there a paper about the project
this effort will be a part of?
- Knowledge prerequisites: programming language(s) to be used, plus
any other particular computer science skills needed.
- Skill level: Basic, Medium or Advanced.
- Project Time: 90-hour, 90, 175 or 350 hours that are a standard 8
weeks long and no longer than 12 weeks.
- Mentors: name + contact details of the lead mentor, name + contact
details of 1 or 2 backup mentors.
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