Andrej Angelovski@Rexor

Eco Mentor is a groundbreaking AI-powered platform focused on environmental education and sustainability. Aimed at fostering a deeper understanding of ecological concerns and promoting sustainable practices, the platform caters to individuals eager to make environmentally conscious choices. The core problem addressed is the gap in accessible, personalized environmental education and community involvement in sustainability initiatives. Eco Mentor offers a solution by integrating AI to deliver customized learning experiences, connecting users with local eco-friendly projects, and providing interactive challenges and tools for eco-conscious living. Unique features include a real-time impact visualization of users' eco-actions, a forum for sharing experiences, and AI assistance for eco-friendly shopping. The platform targets environmentally conscious individuals, educators, and students, making sustainability an engaging, collaborative journey.

Bioinformatics is a new type of science where scientists study biology using vast databases of DNA and protein sequence, protein structure, and many other kinds of data. We propose to develop a new AI system to help bioinformaticians study cancer by using an LLM to extract data from scientific abstract. For this project specifically we are looking at a new platform to cure cancer, Bispecific T-Cell Engagers (BiTEs), that has a potential to be applicable to a wide variety of cancers. We designed a AI chatbot to help bioinformatics researchers search for suitable antigens for specific cancers that could be targets of the BiTEs. For this demo, we prove out our idea by searching for antigens of uveal melanoma, the first cancer that was treated by this method using the drug Tebentafusp. We limit our search to before 2000 so as to be before any potential development of Tebentafusp.

Incorporating the HHEM Vectara RAG, our project sheds light on the impact of query structuring on sensitivity, with the goal of minimizing medical inaccuracies and enhancing patient care safety. This endeavor has led to the development of four pivotal components: Synthetic Data Custom GPT: This element is tasked with generating artificial medical data, thereby expediting the testing procedures. Data Query Custom GPT: Through the use of a RAG system, this component retrieves synthetic data and applies various transformations. These alterations enable us to assess the data's vulnerability to inaccuracies. HHEM-Vectara Query Tuner: This tool is designed to evaluate the transformed data, determining how adjustments to query structure influence the likelihood of errors. Agent Model Evaluation: This phase involves the scrutiny of mixed normal and specific models, including mixtral normal, mixtral crazy, gemini, phi2, and zephyr, to gauge the impact of query modifications on the precision of results. Our software serves as a crucial experimental platform, providing invaluable insights into how even minor modifications and model changes can significantly affect the retrieval of medical data.