  Machine learning has emerged as a powerful tool for scientific discovery,
enabling researchers to extract meaningful insights from complex datasets. For
instance, it has facilitated the identification of disease-predictive genes
from gene expression data, significantly advancing healthcare. However, the
traditional process for analyzing such datasets demands substantial human
effort and expertise for the data selection, processing, and analysis. To
address this challenge, we introduce a novel framework, a Team of AI-made
Scientists (TAIS), designed to streamline the scientific discovery pipeline.
TAIS comprises simulated roles, including a project manager, data engineer, and
domain expert, each represented by a Large Language Model (LLM). These roles
collaborate to replicate the tasks typically performed by data scientists, with
a specific focus on identifying disease-predictive genes. Furthermore, we have
curated a benchmark dataset to assess TAIS's effectiveness in gene
identification, demonstrating our system's potential to significantly enhance
the efficiency and scope of scientific exploration. Our findings represent a
solid step towards automating scientific discovery through large language
models.
