AMBLER, Pa. - TelAve -- Artificial intelligence is reshaping how bench scientists work with laboratory data, according to Roohid Parast, a translational scientist at Johnson & Johnson's Springhouse, Pennsylvania facility who is also pursuing an MBA with a concentration in AI. Parast says the shift is visible across nearly every stage of the research process, from data generation to final reporting.

Flow cytometry and single-cell RNA sequencing are two areas Parast points to directly. Both techniques generate large, high-dimensional datasets, in some cases tracking dozens of cell surface markers or gene expression patterns across thousands of individual cells. Historically, processing that data required scientists to write and maintain custom analysis scripts by hand. AI-assisted coding tools are increasingly able to help generate and refine that code, lowering the barrier for bench scientists to build their own clustering and gating pipelines without needing a dedicated computational biology team for every project.

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"The data hasn't gotten simpler, but the tools to work with it have gotten more accessible," Parast said. "A bench scientist who understands their biology well can now build a reasonably sophisticated analysis pipeline with AI assistance, where a few years ago that work would have required handing the data off to a specialized bioinformatics group and waiting."

Parast also points to multiomic integration, linking serum proteomic data with single-cell transcriptomic data from the same samples, as an area where AI tools are lowering the technical barrier to entry. Combining data types that were once analyzed in isolation can surface relationships in disease biology that neither dataset would reveal on its own. He notes that AI tools are also being used further downstream, helping scientists build internal dashboards to track samples through a study, generate slide decks for presenting results, and draft written reports for audiences outside the immediate research team.

"None of this replaces scientific judgment or domain expertise," Parast said. "But it changes the pace at which a bench scientist can move from raw data to a defensible interpretation, and it puts more of the analysis and communication work directly in the hands of the people closest to the biology."

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Parast holds a BS in Biochemistry from Temple University and works on inflammatory disease research, including Crohn's disease, ulcerative colitis, and psoriatic arthritis. He sees the convergence of AI and bench science as a continuation of his MBA coursework, which focuses on how organizations adopt and apply new technology.

LinkedIn: https://www.linkedin.com/in/roohid-parast
Website: https://roohidparast.github.io
Google Site: https://sites.google.com/view/roohidparast
ResearchGate: https://www.researchgate.net/profile/Roohid-Parast
Substack: https://roohidparast.substack.com