New things to pursue
Previously unseen biology can suggest therapeutic targets, biomarkers, diagnostic approaches, or opportunities for new intellectual property.
AI · BIOLOGY · SCIENTIFIC DISCOVERY
Signals into
Discovery
I'm Kuan-Ting (Woody) Lin. I find biological signals that existing tools and assumptions overlook—and turn them into mechanistic understanding and opportunities for better therapies.
THE DISCOVERY STORIES
Turning data into discovery.
Explore the question, the discovery, and the evidence behind it.
01
“One pair isn’t data”
Oncogene · 2014
Discovery from one patient: one tumour and one matched normal sample.
Discovery can begin with one pair. Validation must go further.Read the story →02
“The gene didn’t change”
Genome Research · 2018
Discovery at scale: approximately 6,000 RNA-seq samples.
An unchanged total can hide a biological switch.Read the story →03
“The mutation was rare”
Nature · 2019
Revisit accepted results and follow the evidence to mechanism.
A published frequency depends on the analysis behind it.Read the story →04
Coming soon
WHAT I BRING
Expanding what’s possible
A new technology creates possibilities. Scientific judgment determines which possibilities are real, what they mean, and which deserve investment.
The examples highlighted here emphasize work that can be shared publicly; much of my biotechnology experience has involved proprietary discovery programs and platform development.
Better ways to decide
Mechanistic understanding can sharpen patient-stratification hypotheses, improve experimental-model choices, and focus drug-development decisions.
Earlier evidence about what is real
Rigorous testing can establish a promising direction, expose a limitation, or show when a different question is more valuable.
What I contribute
I bring experience across biological data, computational methods, and drug discovery to help teams choose consequential questions, evaluate new capabilities, and recognize opportunities they may not yet know to look for.
I work with computational and experimental colleagues to define what evidence would change a decision, develop the analysis needed, and use the results to guide what comes next. For AI, that means testing what a model contributes to discovery. For drug discovery, it means connecting biological evidence to priorities, experiments, and programme decisions.
Let’s connect ↗