Weiwei Zhai

Two historians, a biologist and a bioengineer from UC Berkeley have won illustrious Guggenheim Fellowships and will pursue independent work under “the freest possible conditions.” news.berkeley.edu/2026/04/17/fou…

Genome-wide genealogies reveal deep admixtures forming modern humans. Funny name to call the method for "GhostBuster" biorxiv.org/content/10.648…

@zhizhid @Nature thank you very much, Degui!

@weiwei_beijing @Nature Congratulations Weiwei, Really cool study!

Super excited to publish this study on EBV and human interaction driving high incidence of nasopharyngeal carcinoma in Southern China. at @Nature nature.com/articles/s4158… with Miao XU, Jianjun LIU, Zhonghua LIU。

Stem Cell Reports is pleased to announce the appointment of Hongmei Wang, Ph.D., as an Associate Editor. Dr. Wang is a professor of State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences (CAS). ow.ly/hcoj50YGw85

Such a great honor for me to start a new chapter of my career as a Research Assistant Professor at Purdue University and to co-lead the Carpenter-Shen lab with @DrAnneCarpenter! More details: linkedin.com/posts/runxi-sh…

We’re pleased to announce an exciting lineup of confirmed speakers for the 2026 HCA Asia Meeting in Perth, Australia, 2–4 November. Find out more about our speakers here: buff.ly/LAdNBib #HCA2026Asia #Genetics #SingleCell #Spatial

Cancer is an evolutionary process. We've known this for decades, but we didn't know whether its complexity was tractable. We now know we can measure and sometimes predict cancer evolution in patients from molecular ('omics) data, although with still suboptimal precision. A way forward is combining evolutionary theory (maths and concepts from theoretical population genetics) with machine learning & AI, the latter filling the theory “gaps” when solutions from first principles are out of reach. Building predictive models of cancer evolution means we may be able to control the disease, designing evolution-aware therapies that prevent or delay drug resistance. Come to learn more about this at #AACR26 session: ED53 - Cancer Systems Biology, Ecology, and Evolution. abstractsonline.com/pp8/#!/21436/s…

Super excited to publish this work: STEER: Decoupling kinetics with Spatial-Temporal Explainable Expert Model for RNA velocity inference, academic.oup.com/nsr/advance-ar… with @LiangMa_IOZ at National Science Review @NSR_Family

Join us on Tuesday, April 7 at 11am PDT to hear Kamila Naxerova @naxerova from Harvard Medical School in the next installment of the Mathematical and Computational Modeling of Cancer Seminar. Livestream link to follow shortly.

This paper took us 13 years and is one of the longest papers ever in Cell. Check it out & judge for yourself cell.com/cell/fulltext/…

After 20 years, 58 generations and more than 30,000 cloning attempts, a team of researchers has hit the limit on the number of times a single mouse can be serially re-cloned go.nature.com/47hykPQ

Excellent. Genomic history of early dogs in Europe nature.com/articles/s4158…

By analyzing DNA data from over 900,000 people, a new study found that some of the most common viruses hiding in the body vary with age, sex, and the seasons — and that genes influence the long-term effects these viruses have on our health. broad.io/virome-news

We wrote a review on using machine learning to study evolutionary genetics and molecular evolution in Trends in Genetics @TrendsGenetics . It is open access—please read it if you are interested in this topic sciencedirect.com/science/articl…

Human organoids as 3D in vitro platforms for drug discovery: opportunities and challenges rdcu.be/e8qhG nature.com/articles/s4157… This Review in the March issue discusses generating and maintaining organoids, and their applications in disease modelling and drug screening

Everyone is talking about personalized mRNA cancer vaccines. I want to share two recent Nature papers that cut through the excitement and reveal something the viral posts aren't telling you: the approach works — but only in patients whose immune system actually responds to the vaccine. In the PDAC trial, that was half. Papers: — TNBC-MERIT trial (Nature 2026): nature.com/articles/s4158… — PDAC 3-year follow-up (Nature 2025): nature.com/articles/s4158… Here's the exact number that explains why. The PDAC trial: at 3.2 years median follow-up, vaccine responders had median recurrence-free survival that was never reached. Non-responders: 13.4 months. HR = 0.14. The T cell memory is real — some clones are projected to persist for over a decade. The TNBC trial: 10 of 14 patients remained relapse-free at 5 years. One patient has been in remission for over 6 years, with neoantigen-specific T cells still circulating at ~2% of her CD8 repertoire. So what separates responders from non-responders? Across both trials: only 41 of 251 neoantigens actually triggered a T cell response. That's 16%. Each vaccine encodes up to 20 neoantigens — the algorithm's best guess at which tumor mutations will be immunogenic. Most don't work. Half the PDAC patients didn't respond — not because they couldn't mount an immune response (they responded fine to concurrent COVID vaccines) — but because their selected neoantigens happened to miss. This is the core unsolved problem: predicting, from sequence alone, which mutations will produce peptides that a specific patient's immune system will actually recognize. It sounds like an MHC binding problem. It isn't. Tools like NetMHCpan handle binding affinity reasonably well. What they miss is the full causal chain: 1. Proteasomal processing — will the protein actually be cleaved into this exact peptide? 2. TAP transport — will it reach the ER for MHC loading? 3. HLA-peptide stability — across the patient's specific HLA alleles (10,000+ variants in the population) 4. T cell repertoire availability — has central tolerance already deleted the clones that would recognize it? 5. Tumor clonal architecture — is this mutation in every tumor cell, or just 30%? Targeting subclonal neoantigens leaves most of the tumor untouched. Every step is a filter. Current prediction stops at step one. Compounding everything: average manufacturing time in the TNBC trial was 69 days (range: 34–125) from sample to vaccine release. For pancreatic cancer, where non-responders recur at 13.4 months post-surgery, that's not a footnote. It's a window closing. The good news: the T cell biology is sound. The mRNA platform works. The immunology is spectacular — when it works. The bottleneck is the first step: choosing which 20 neoantigens go in the vaccine. Get that prediction right, and the responder rate moves. This is where AI in cancer immunotherapy has to go next. Not mRNA design. Not LNP formulation. Immunogenicity prediction — integrating mutation calling, HLA typing, T cell repertoire sequencing, and single-cell tumor expression simultaneously, as a causal inference problem, not a binding affinity lookup. We don't have a model that does this well. That's the gap.

Whole-embryo spatial transcriptomics at subcellular resolution from gastrulation to organogenesis | Science science.org/doi/10.1126/sc…

Jian Ma (@jmuiuc) and I sent out initial decisions for all #ISMB2026 Proceedings papers this week. Huge effort: ~1800 reviews + 400 meta-reviews by 400+ PC members and 24 area chairs in just 7 weeks. Thanks a lot to everyone involved! iscb.org/ismb2026/home

Spatiotemporal transcriptome atlas of developing mouse lung doi.org/10.1016/j.scib…