This blog post contains contributions from Dale Bodian and Jason Warner. The most expensive AI mistakes usually come from forcing complex methods onto datasets that aren’t ready for them. In…

We’ve said it before, and we’ll say it again: AI rewards preparation, not ambition. Teams that invest in data quality and experimental design will always outperform those who just dive…

RNA-Seq analysis often seems straightforward, like just pushing a button. But to do it well, especially for finding new drugs, you need more than just a basic approach. Every study, every experiment,…

Every year in early spring, a major opportunity arrives for the biomedical research community: the annual allocation of grant funding. This money is designated as an investment into accelerating drug…

A practical, biology-first look at AI in drug discovery, where it helps, where it fails, and how Diamond Age helps teams avoid hype-driven mistakes.

“We have results from our clinical trial, and need Diamond Age’s help in analyzing the data.” These words are like music to my ears. Clinical trial projects are exciting to…

Diamond Age Data Science and the Boston Computational Biology and Bioinformatics Meetup recently packed the house for a discussion on one of the buzziest topics in science today: AI in computational…

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