collaborators

5 papers

cs.AI2026

EpiBench: Verifiable Evaluation of AI Agents on Epigenomics Analysis

Harihara Muralidharan, Reema Baskar, Soo Hee Lee +2

We introduce EpiBench, a verifiable benchmark for short-horizon epigenomics analysis. EpiBench evaluates whether agents can make well-defined analysis decisions from realistic work…

cs.AI2026

Verifiable Benchmarking of Long-Horizon Spatial Biology

Ian Diks, Harihara Muralidharan, Tim Proctor +1

AI agents are increasingly useful for biological data analysis, but existing benchmarks mostly test broad biological knowledge, executable workflows, or localized analysis steps ra…

q-bio.GN2026

scBench: Evaluating AI Agents on Single-Cell RNA-seq Analysis

Kenny Workman, Zhen Yang, Harihara Muralidharan +2

As single-cell RNA sequencing datasets grow in adoption, scale, and complexity, data analysis remains a bottleneck for many research groups. Although frontier AI agents have improv…

cs.AI2026

SpatialBench: Can Agents Analyze Real-World Spatial Biology Data?

Kenny Workman, Zhen Yang, Harihara Muralidharan +1

Spatial transcriptomics assays are rapidly increasing in scale and complexity, making computational analysis a major bottleneck in biological discovery. Although frontier AI agents…

q-bio.OT2025

Contributions of the Petabyte Scale Sequence Search Codeathon toward efforts to scale sequence-based searches on SRA

Priyanka Ghosh, Kjiersten Fagnan, Ryan Connor +35

The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institut…