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Timothy Smith on Muck Rack

Timothy Smith

Green Bay
As seen in: The New York Times, Nature, Wiley Online Library, PLOS Medicine, PLOS ONE, MDPI, ACS Publications, Seeking Alpha, Pediatrics, Dove Medical Press and
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Articles

Pangenome-based structural variant imputation enables large-scale genotype-phenotype studies in dairy cattle

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By Liu Yang, Junjian Wang, Kristen Kuhn, Geoffrey Zanton, Mahesh Neupane, Clarissa Boschiero, John Cole, Bingjie Li, Congjun Li, Ransom L. Baldwin, Benjamin Rosen, Timothy Smith, Jicai Jiang, Lingzhao Fang, Li Ma, George Liu, Curtis P. Van Tassell
| Nature Verified
Abstract Pangenomes of several species have been assembled recently, facilitating the detection and genotyping of structural variants. As part of the FarmGTEx Project, we previously constructed a Holstein pangenome (H20D) based on 40 phased haploid assemblies. Here, we use this breed specific pangenome to genotype 93,059 structural variants from whole-genome sequences of 1,571 cattle. We then develop a Holstein pangenome variation imputation reference panel we name HolPIP.
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Publisher Correction: Oral small molecule GLP-1 receptor agonist aleniglipron in people with overweight or obesity: a randomized, double-blind, placebo-controlled phase 2b trial

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By Julio Rosenstock, Ildiko Lingvay, Donna Ryan, Ania M. Jastreboff, Robert F. Kushner, Andres Acosta, Thomas C. Blevins, Timothy Smith, Lisa Connery, Yao Li, Mike Liu, Aline Barth, Jamie Butcher, Antonio Civitarese, Huibin Yue, Blai Coll
| Nature Verified
Correction to: Nature Medicine https://doi.org/10.1038/s41591-026-04476-6, published online 5 June 2026. In the version of the article initially published, in Fig. 3b, the Aleniglipron 120 mg column initially listed LS mean change as “–12.1” and has been updated to read “–12.7,” while in Fig. 4, the data was missing from the bottom-right panel, and all arrows were missing from the “Vomiting” panels. Figures 3 and 4 have now been corrected in the HTML and PDF versions of the article.
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Machine learning in automatic detection of chordoma signature, postoperative residuals, and prognosis of skull base chordomas

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By Richard J Mannion, Robert MacFarlane, Robert Macfarlane, Patrick R. Axon, Neil Donnelly, Daniele Borsetto, Wenya Linda Bi, Carleton E. Corrales, Ossama Al-Mefty, Timothy Smith, Ari Ercole, Jonathan Coles Verified, Daniela Stastna, James R. Tysome, Ryan Chrenek, Kaasinath Balagurunath
| Nature Verified
Abstract Skull base chordomas are rare, locally invasive tumors that remain a diagnostic and therapeutic challenge. We developed a machine-learning (ML) radiomics model to (i) distinguish chordoma from chondrosarcoma and skull base background, (ii) differentiate true postoperative residual tumor from treatment-related changes, and (iii) predict 2-year progression-free survival (PFS). In this retrospective, dual-center study, 61 patients underwent surgery between 1998 and 2023.
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