Papers for

data science teams in pharmaceuticals

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Harness automates optical screening pipeline to improve gene analysis

Harness-Driven Agentic Operation of Optical Pooled Screen Analysis

Abstract: Optical pooled screens connect genetic perturbations to changes in cell morphology and behavior, but turning the microscopy data into interpretable results takes complex, multi-day analyses. Established pipelines process this data, yet researchers still select parameters, inspect outputs, and resolve failures for each experiment. Here, we present Brieflow-auto, a pipeline-specific harness implemented as a Claude Code plugin that lets a coding large language model (LLM) operate Brieflow, an open-source end-to-end pipeline for optical pooled screening data. A researcher supplies experimental facts and approves the launch. An agent tunes parameters in an interactive notebook, and the harness validates the configuration, monitors execution, recovers from common failures, and checks output quality. For pipeline defects, the agent opens upstream issues proposing backward-compatible fixes for the broader community. In five previously analyzed screens, harness runs yielded more cells with a confident single-gene assignment than the original hand analyses. On a screen the pipeline was not designed for, the harness turned each failure into a reviewed upstream fix. More broadly, we propose that wrapping an established pipeline in a harness can substantially accelerate its use and the long-term reliability of its codebase.

Thu 15 OctHuman-Computer Interaction
The gist
Analyzing images from experiments that change genes in cells can take many days and lots of manual work. The authors created a tool called Brieflow-auto that uses an AI assistant to run and manage this analysis automatically. It checks for problems, fixes common errors, and even suggests improvements to the software. Their tests showed this approach found more useful data and handled unexpected issues better than humans working by hand. This method could make these complex experiments easier and faster for scientists to use.
Open → 2610.22617v1