WHAT CONNECTS THE WORK
Look beyond predefined measurements.
Look beyond a gene’s total activity.
Look beyond a mutation’s label.
Look beyond a model’s intended use.
(Coming soon after publication)
Extensive data can still leave the important biology undiscovered. My contribution is recognizing what the data could reveal, developing the analysis needed to test it, and helping turn the result into the next scientific decision.
THE SAME QUESTION, NOW APPLIED TO AI
The shift from microarrays to RNA sequencing taught me to ask what new biology a technology makes accessible. I bring the same question to AI: what useful signal might a model contain beyond the task it was designed to perform?
The Discovery Loop describes how I approach that question. Revisit overlooked evidence. Test an unexpected capability. Let the result change the explanation—and reveal the next question worth asking.
Define what we need to understand. An unexpected observation, an unmet need, or a new technology can open a question that current methods have not answered.
One discovery, step by step
AFMID example: Could the choice of RNA version reveal liver-cancer biology that total gene activity obscures?
Each discovery opens the next question. Evidence changes what we ask and how we investigate. A result may support an explanation, rule it out, or reveal a new possibility. The next cycle starts from what we have learned.
MY VISION FOR AI DISCOVERY
What defines an AI innovator is not the ability to generate known hypotheses, but the ability to turn an unexpected insight into a new discovery—building and testing the evidence until it changes what we understand about biology.
My question: how can a non-specialist see why the SRSF2 discovery mattered, without losing the distinctions that make the evidence trustworthy?
I identified the story worth telling, challenged vague explanations, pointed to the age-specific finding in cBioPortal, and asked that the claims be checked. I also set the boundaries around personal information and collaborative credit.
Astra helped inspect the paper, calculate frequencies from public data, simplify the explanation, and implement the interactive website. My feedback shaped what to investigate and how the findings should be presented.
Similar frequencies across three cohorts support a recurring finding. They do not establish an identical frequency in every patient population.
Inspect Figure 1b–d · Nature 2019 ↗The value of the interaction was in turning a scientific insight into a clear explanation whose numbers, assumptions, and limits can be inspected.
Fixed public-data snapshot, 10 September 2026; these controls select verified summaries, not a live AI response. cBioPortal study aml_ohsu_2018, mutation profile aml_ohsu_2018_mutations, SRSF2 gene 6427. Only mutation-profiled samples with recorded age at diagnosis were included. Patients were counted once and marked mutation-positive if any included sample carried an SRSF2 mutation. The age filter is ≥70. The all-age comparison includes all recorded ages, with no additional disease-stage filter.
These descriptive percentages do not establish a causal age effect, predict an individual’s outcome, or replace a formal clinical analysis. Astra helped communicate and inspect published research here; it did not make the original biological discoveries.