AI

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.

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 biology and drug discovery. Taking a grounded perspective, the panel got real about what AI can do today, where it is still tripping up, and… Read More »The Real Story on AI in Drug Discovery

Call it snark or pragmatism, but a recent BioBuzz panel, “Leveraging AI Across the BioPharma Value Chain,” brought something rare to the AI conversation: honesty. The panelists didn’t worship at the altar of algorithms. Instead, they asked smart questions. When does AI really add value? When is it just a high-tech answer in search of… Read More »You Don’t Need AI for That

At Precision Med TRI-CON, I joined a panel on “Omics, Data, and AI in Precision Medicine,” where we tackled a big, messy question: What’s keeping AI from delivering on its full potential in precision medicine?  Spoiler alert: it’s not the algorithms. It’s the data. Or more specifically, the glaring lack of clean, curated, context-rich datasets.… Read More »AI Isn’t the Problem. Your Data Is.

by Eleanor Howe and Michael DeRan After attending Cellarity’s medSCAI Symposium on single-cell research, AI, and data-driven drug discovery, we came away buzzing with inspiration over new possibilities for our clients at Diamond Age. The event was loaded with insights and tech designed to shake up bioinformatics and open real doors for biotechs looking to… Read More »Big Data Potential for Small to Mid-Sized Biotechs

Lately, I’ve been reflecting on the rise of AI in the life sciences, especially after attending the International Conference on Intelligent Systems in Molecular Biology (ISMB). AI is a double-edged sword in our field; while it does hold promise for advancing the field, like automating data analysis and uncovering complex patterns in large datasets, it… Read More »AI in Life Sciences: A Roadmap for Integration

By analyzing the gene expression profiles of individual cells within a population, single-cell RNA sequencing (scRNA-seq) helps us to understand heterogeneity within cell populations, discover rare and novel cell types, and identify cell states associated with various biological processes and diseases. These unique advantages of scRNA-seq relative to previous measures of gene expression allow us… Read More »Harnessing GPT-4 for Enhanced Cell Classification