How do cells come together to make tissues? To answer this question, using AI, the Welch laboratory has developed a new computational tool that incorporates space and time into models of cell fate transition. This modelling constitutes a key step toward characterizing how interactions among neighboring cells, local niche factors and cell migration contribute to tissue development. It has been published in Nature Biotechnology (July 2025).

Nine Medical School faculty members and nine research staff members have been recognized for their contributions to teaching and research with Endowment for Basic Sciences (EBS) Awards for 2025. Each of the nine UMMS basic science departments select a winner for each award. This year’s recipients received their awards June 12 during a ceremony in the Medical School.

On June 9, 2025, Dr. Yang defended his dissertation titled: “Holistic Integration of Deep Learning Models for Mass Spectrometry-Based Peptide Identification.” His mentor was professor Alexey Nesvizhskii.

Joel Eliason, Ph.D., developed Bayesian statistical tools to analyze and interpret the spatial relationships between the different types of cells present in a tumorous environment. His results were presented in his Ph.D. dissertation, titled “Multiscale statistical models for understanding tumor microenvironment heterogeneity,” and defended on May 28, 2025. His mentor was Arvind Rao, Ph.D., DCMB, and he also worked closely in collaboration with Michele Peruzzi, Ph.D., a biostatistician in the School of Public Health and a CCMB member.

Every year, the Endowment for Basic Sciences (EBS) recognizes a faculty and a research staff with an award. This year, in DCMB, AJ Wing, PhD, was recognized with a Teaching Award, and Greg Farnum with a Research Staff Award.

Professor Kin Fai Au and his lab members Xiaoyu Cai, Qi Gao, Haoran Li, Puwen Tan, Dingjie Wang, and Yunhao Wang, with partners from Ohio State University, developed a new software that improves the accuracy of the quantification of gene isoforms for complex genes. Their software, called miniQuant, ranks genes with the uncertainty of isoform quantification. It integrates the complementary strengths of long reads and short reads of RNA sequencing data with optimal combinations in a gene- and data-specific manner to achieve more accurate isoform quantification. These findings are published in Nature Biotechnology.