Edison Scientific, Inc

This week at GTC, we announced our partnership with @nvidia. Edison is committed to build at the frontier of scientific reasoning. We partner with NVIDIA across the AI stack in training and benchmarking capabilities to accelerate scientific research. Our latest partnered release is on BixBench-Hypothesis, a new benchmark focused on analytical judgment under ambiguity, or the ability to pursue a hypothesis with an open-ended goal. See our release blog post in the comments. More from our CEO @SGRodriques on why this partnership matters:

Read more: edisonscientific.com/articles/accel…

Edison Analysis Agent can access publicly hosted RNA-seq datasets, perform transcriptomic analyses, and draw accurate biological conclusions. Here we use the Edison Analysis Agent to reanalyze a recently released dataset on Autism Spectrum Disorder (ASD). Identifying how risk-associated mutations mechanistically alter brain development and contribute to ASD remains a significant challenge. In their recent Nature paper, Gordon et al. tackled this problem using patient iPSC–derived cortical organoids and time-resolved bulk RNA-seq, revealing genotype-linked expression similarities of ASD forms and their strongest divergence from normal organoids at the earliest stages. In our new post, the Edison Analysis Agent autonomously reanalyzed the public dataset and reproduced both key results, recovering genotype-driven clustering of ASD forms and independently recapitulating their “early divergence, later convergence” trajectory across development.

See the link to the paper: nature.com/articles/s4158… And the analysis: edisonscientific.com/articles/repro…

Not ready for a full Kosmos run? Start with a mini-discovery in Edison Analysis. We asked Edison Analysis to explore the Genomics of Drug Sensitivity in Cancer (GDSC) public dataset. In a single run, it retrieved and explored the data for sensitivity patterns, formulated a BCL2-dependency hypothesis in hematological cancers, and tested the hypothesis using independent CCLE expression data.

Read more here: edisonscientific.com/articles/ediso…

Today we are announcing a major update to Edison Literature, our scientific deep research agent. It represents a significant advancement over our previous PaperQA2 algorithm: it enables deeper reasoning over 100s of scientific documents, can retrieve information from figures and tables, and is state-of-the art among other deep research systems on scientific tasks.

GIF

Read more here: edisonscientific.com/articles/ediso… Try our platform here: platform.edisonscientific.com

Real-time single molecule sensing with nanopores has the potential to transform several fields including precision medicine and diagnostics, drug discovery, protein characterization, and chemical manufacturing. In a recent pioneering work advancing the abilities of nanopores, Zhang et al. (2024) introduced a custom-engineered nanopore capable of trapping single amino acids and demonstrated that molecular volume is the primary determinant of ionic current blockade. Here, we show that the Edison Analysis Agent can autonomously reproduce this key biophysical finding using the authors’ published nanopore time-series recordings—recapitulating the quantitative relationship between amino acid volume and blockade amplitude. Starting from raw current traces, the agent identified open-pore baselines and blockade levels, computed amino acid–specific blockade current amplitudes, integrated canonical biophysical properties of amino acids from the literature, and performed correlation and regression analyses to independently identify molecular volume as the dominant predictor of current blockade as individual amino acids pass through the nanopore.