# Welcome

# DrBioRight Platform

Overall, we incorporated a multi-agent workflow to build hierarchical agent teams using a graph architecture. This framework can better organize the multi-agent system and streamline the development process (see Methods). Each team consists of one or more agents or tools. For example, the multi-omics data analysis team uses a heatmap to provide a dataset overview and a survival analysis tool to link proteins with patient survival data. A correlation analysis tool will perform association analysis between features including protein expression, mutations, and clinical variables. A supervisor routes team-specific questions to appropriate tools for task execution and analytic results. Each agent is powered by a model coupled with task specific prompts.

To demonstrate its utility, we present an illustrative example where users can effortlessly query, “Please generate a heatmap for protein expression data of the current dataset.” In response, DrBioRight dynamically processes the data and calls the corresponding heatmap plugin to generate an interactive heatmap (FigureA). Similar to other interactive plugins we have developed, the heatmap plugin can efficiently handle large datasets. It offers a comprehensive global overview along with numerous features (such as selection, zoom in/out, searching, 2D/3D scatter plots, pathway mapping, and linking to external resources) to facilitate effective data exploration. For a more detailed analysis, users can further ask, “Could you please show me the correlation between AKT2PS474 and IL6 expression?” DrBioRight then extracts the data, performs the corresponding statistical analysis, and presents the results in a clear scatter plot. Leveraging the same dataset, users can conduct a survival analysis by inquiring about the correlation between a protein and the patient survival time, followed by visualization through Kaplan-Meier plots. In contrast to the previous analytic modules at TCPA, DrBioRight distinguishes itself by offering versatile analyses, including customizable interactions with the chatbot. For instance, post a survival analysis using the full cohort, users can delve into specific associations within male or female patients or change the colors in a plot. Another noteworthy feature of DrBioRight is its seamless transition between analytics-driven and general questions. As depicted in FigureA, users can request the chatbot to summarize the results. These features collectively position DrBioRight as a revolutionary tool, providing unparalleled flexibility and customization in data analysis.

The system architecture of DrBioRight comprises three integral components: (i) a non-SQL database, (ii) a back-end LLM-powered analytics module, and (iii) an interactive chat interface (FigureB). To start an analysis, a user simply begins by selecting a disease (e.g., lung adenocarcinoma [LUAD]). Then, the chatbot automatically links relevant multi-omics data to the user's project space, primed for querying and analysis. The back-end LLMs discern user intent, distinguishing between general inquiries and questions requiring code generation or bioinformatics analysis. DrBioRight outputs a logical flow based on a chain-of-thought approach to enhance user understanding. In the back end, LLMs generate text-based answers or programming scripts on the fly. Before submission to the job queue, the platform reviews and validates codes, autonomously correcting common errors like missing libraries or incompatible package versions. Following successful result generation, the user-friendly chat interface displays the outcomes. For ongoing improvements, we integrate a rating function that allows users to evaluate analytic results, and the user feedback then guides iterative refinements to enhance LLM capabilities.