Sinitskiy, A.

This study presents a case analysis of using AI systems as co-pilots in biological research, focusing on functional protein networks in invasive colorectal cancer. We used public proteomic data alongside ChatGPT, GitHub Copilot, and PaperQA to automate parts of the workflow, including literature review, code generation, and network analysis. While AI tools improved efficiency, they required expert guidance for tasks involving complex metadata, domain-specific parsing, and reproducibility. Our analysis identified cytoskeleton- and signaling-related networks in invasive cancer, aligning with known biology, but attempts to distinguish invasive from non-invasive cases produced inconclusive results. An attempt to conduct fully automated research using Agent Laboratory failed due to hallucinated data, misinterpretation of research goals, and instability as the complexity of the underlying LLM increased. These findings show that current AI can assist but not replace human researchers in complex biotech studies.