Ph. D., Johns Hopkins University, completed 2025, and historical trombone specialist
There is a lot of new work coming out relevant to predicting transcriptomic responses to perturbation. I’m way behind on reading. Since things are heating up, and I got a job doing something totally different, I will remain behind on reading for the foreseeable future. I’m sorry that I can’t go into my usual level of detail on all this newer work, but here’s my attempt to provide a curated and…
The problem with gene-wise characterization of genetic interventions
Bias-variance tradeoffs for virtual cell evals are weird
The comp bio job market is scarier than usual: 2023 was a biotech bankruptcy bloodbath, 2024 was a little better, and as far as layoffs, 2025 has been worse. Academic and government sectors are also reeling from government cuts, and in the two places I have recently lived (Massachusetts and Maryland), cuts have specifically targeted large biomedical research employers (Harvard and NIH). The tech…
In October 2024, I twote that “something is deeply wrong” with what we now call virtual cell models. A lot has happened since then: modelers are advancing new architectures and mining new sources of information; evaluators are upping their game with deeper follow-up and an open competition; and synthetic biology wizards are building new datasets. Let’s see what’s cookin’.
Linkage disequilibrium score regression (LDSC) is a workhorse technique at the intersection of quantitative genetics and functional genomics. The core use of S-LDSC is to detect and quantify functional enrichment of genetic associations; for example, “Are STAT1 binding sites enriched for genetic risk of Crohn’s Disease?” or “Are CNS-specific enhancer regions enriched for effects on psychiatric…
Stratified linkage disequilibrium score regression (S-LDSC) is a workhorse technique at the intersection of human genetics and functional genomics, but its exposition is heavily genetics-coded. In cell biology and functional genomics, it is less well-known and well-understood than would be optimal. This post is part of a series introducing many flavors of S-LDSC for the newcomer.
Stratified linkage disequilibrium score regression (S-LDSC) is a workhorse technique at the intersection of human genetics and functional genomics, but its exposition is heavily genetics-coded. In cell biology and functional genomics, it is less well-known and well-understood than would be optimal. This post is part of a series introducing many flavors of S-LDSC for the newcomer.
Stratified linkage disequilibrium score regression (S-LDSC) is a workhorse technique at the intersection of human genetics and functional genomics, but its exposition is heavily genetics-coded. In cell biology and functional genomics, it is less well-known and well-understood than would be optimal. This post is part of a series introducing many flavors of S-LDSC for the newcomer.
Stratified linkage disequilibrium score regression (S-LDSC) is a workhorse technique at the intersection of human genetics and functional genomics, but its exposition is heavily genetics-coded. In cell biology and functional genomics, it is less well-known and well-understood than would be optimal. This post is part of a series introducing many flavors of S-LDSC for the newcomer.