SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot." It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. AVAILABILITY AND IMPLEMENTATION: GENEVIC is publicly accessible at https://genevicanath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git).

Multi-modal sentiment detection from natural video/audio streams has recently received much attention. I propose to use this multi-modal information to develop a technique, Sentiment Coloring , that utilizes the detected sentiments to generate effective responses. In particular, I aim to produce suggested responses colored with sentiment appropriate for that present in the interlocutor's speech. To achieve this, contextual information pertaining to sentiment, extracted from the past as well as the current speech of both the speakers in a dialog, will be utilized. Sentiment, here, includes the three polarities: positive, neutral and negative, as well as other expressions of stance and attitude. Utilizing only the non-verbal cues, namely, prosody and gaze, I will implement two algorithmic approaches and compare their performance in sentiment detection: a simple machine learning algorithm (neural networks), that will act as the baseline, and a deep learning approach, an end-to-end bidirectional LSTM RNN, which is the state-of-the-art in emotion classification. I will build a responsive spoken dialog system(s) with this Sentiment Coloring technique and evaluate the same with human subjects to measure benefits of the technique in various interactive environments.