Very excited to share our @ScienceMagazine paper on single-cell #3D #genome reorganization in #Alzheimer's disease. We jointly measured gene expression and 3D genome architecture in individual human brain cells using #GAGEseq, then integrated these data w/ chromatin accessibility and spatial transcriptomics. We uncovered increased #compartment #mingling and distance-dependent rewiring of gene regulatory contacts in AD. We also developed #Hicformer, a transformer-based model that integrates DNA sequence with 3D genome features to predict cell type-specific gene expression and prioritize candidate regulatory elements. Huge kudos to co-first authors @zocean636 and @xinyuelu1999; and many thanks to Zhijun Duan @UW, Hansruedi Mathys @PittTweet, & David Bennett @rushalzheimers for the wonderful collaboration, as well as to all our co-authors. @CarnegieMellon @SCSatCMU @CMUCompBio #AlzheimersDisease #3DGenome #SingleCell #AI science.org/doi/10.1126/scie…

I spoke with @Nature, alongside several other researchers, about why many AI researchers still choose academia despite the growing pull of industry. Academia's unusual freedom to choose which questions are worth pursuing is powerful. But I do not think academia and industry need to be opposites. As AI reshapes science, we need models that let them co-evolve, combining academia's long-term freedom and openness with industry's resources, scale, and ability to rapidly translate ideas into impact. nature.com/articles/d41586-0…

Happy to see our recent paper featured by the NIH as part of "NIH Research Matters." nih.gov/news-events/nih-rese…

I shared before that my only paper as a PhD student was published in @GenomeResearch 20 years ago. I'm delighted to share that I recently joined the journal as an editor genome.cshlp.org/about. This feels very personal. As a student, I learned genome biology partly by reading papers by Rick Myers, Evan Eichler, Aravinda Chakravarti, Richard Gibbs, and Anne Ferguson-Smith, and I took Kateryna Makova's evolutionary genomics class in grad school. Now I get to join them in weekly editorial meetings - It still feels a bit surreal. One thing that makes @GenomeResearch special is that its 7 editors are active scientists who discuss new submissions and make editorial suggestions together. We meet every week on Zoom - and yes, we look at submissions over the weekend. I am new - but some editors have served the journal for decades. I've been struck by the seriousness, care, and sense of stewardship they bring to every paper and to the journal as a whole. @GenomeResearch is a nonprofit journal that has published landmark papers and helped shape genomics for decades, while remaining deeply committed to the research community. I am honored to play a small part in what come next. We all have many choices about where to submit our work. Whether you work in comp biology, AI for genomics, genomic technologies, disease genomics, genomic variation, evolutionary genomics, or other areas of genome biology, I hope you continue sending your exciting work to @GenomeResearch

very comprehensive Review on single-cell foundation models from @JiayuanDing, @Xiaojie_Qiu, @TheodorisLab, and colleagues. After going over it, I would encourage the authors to make it more critical rather than primarily an inventory of models. First, I think Fig 1 needs some rethinking. The definition of scFM has become too loose. For example, the actual model unit of GET is celltype pseudobulk aggregated from scATAC-seq, hence not single cell model. Also thanks for highlighting our TissueNarrator, but we certainly do not consider it a bona fide scFM - it is a specialized spatial transcriptomics model built by leverging an existing foundation LLM (Qwen). TissueNarrator is very interesting and we re having lots of fun w/ it but it's not a scFM. Anyway - Fig 1 seems to mix FMs, specialized models built on FMs, and task-specific models into one big family tree. More broadly, I think the more interesting question for a scFM review in 2026 is not how many scFMs now exist or how their architectures differ, but what has the scFM paradigm actually bought us? Since the first wave around scBERT/Geneformer/scGPT, the number of models, parameters, and pretraining cells has exploded, but as a field we are not convinced that capabilities have advanced proportionally. - Where has large scale pretraining enabled something that a strong specialized model could not do ? - Where do scFMs consistently beat strong task specific or even simple baselines? - Where is the broad evidence for true cross-context generalization on biologically interesting tasks? The Review mentions many of these challenges indeed, but I think they should be much closer to the central theme of the article rather than appearing mainly in a later Challenges section :-) I might almost suggest to flip the review around and instead ask: - what have we actually learned after several years and many scFMs? - What claims have survived independent benchmarking? - What has scaled and what has saturated? - What capabilities are genuinely attributable to pretraining and where do we still need much more evidnece? - What important biological problems remain essentially unsolved by scFMs ? Given the current rate of model proliferation, there may be another 50 scFMs by this time next year and this inventory / figure will become outdated very quickly. To me, a more useful and timely "state of the field" critique would be much more valuable and long-lasting than another "model zoo" review .

happy to have contributed to this Perspective @CellCellPress. grateful to @califano_lab for leading this effort and to all coauthors

Agreed. AI can accelerate discovery but It cannot shortcut evidence. Models can move at AI speed but cures still have to be proven in humans. "Most diseases cured in 5-10 years" - that timeline goes beyond the evidence.