2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
Graph-based variant discovery reveals novel dynamics in the human microbiome
Harihara Subrahmaniam Muralidharan, Jacquelyn S Michaelis, Jay Ghurye +4
Sequence differences between the strains of bacteria comprising host-associated and environmental microbiota may play a role in community assembly and influence the resilience of m…