BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250513T115835EDT-2454I9paH9@132.216.98.100 DTSTAMP:20250513T155835Z DESCRIPTION:Decoding cellular dynamics from single-cell data for more effec tive cell fate manipulation\n\nJun Ding\, ÐãÉ«Ö±²¥\n Tuesday Janua ry 11\, 12-1pm\n Zoom Link: https://mcgill.zoom.us/j/85428056343\n\nAbstrac t: In recent years\, the emerging single-cell technologies provide unprece dented opportunities for studying many challenging biomedical problems\, e specially in the cell differentiation and cancer biology areas\, in which there exists tremendous cell heterogeneity. However\, the single-cell data sets generated in those studies are usually high-dimensional\, large-scale \, noisy\, and heterogeneous\, making it challenging for the biomedical sc ientists to directly utilize the single-cell data in support of their heal th science research. In this talk\, I will discuss how to make novel biolo gical discoveries and medical innovations in cell differentiation studies that will eventually benefit public health via analyzing\, modeling\, and visualizing large-scale single-cell genomics dataset with machine learning methods. First\, I will talk about some critical computational challenges in analyzing the single-cell genomics data\, followed by the discussion o f graphical models that I developed to address those challenges (i.e.\, id entifying sub-populations\, trajectory inference\, efficient data/model pr esentation\, gene regulatory network reconstruction). Next\, I will discus s how we innovated the protocol to differentiate lung epithelial cells fro m the induced pluripotent stem (iPS) cells\, with the help of the develope d single-cell computational methods and a computationally guided way to de sign the single-cell experiments. In this study\, we have doubled the lung epithelial differentiation efficiency compared to the state-of-the-art pr otocol by repressing the Wnt pathway (within a specific time window)\, all predicted from the developed computational model.\n\nBio: Jun Ding (https ://www.meakinsmcgill.com/ding/) is an assistant professor in the Departmen t of Medicine\, ÐãÉ«Ö±²¥ Health Centre. Before that\, he was trai ned as a postdoc at the Computational Biology Department\, School of Compu ter Science\, Carnegie Mellon University\, under the supervision of Dr. Zi v Bar-Joseph. In 2016\, he received his PhD. in Computer Science from the University of Central Florida. His research focuses on developing computat ional methods to drive biological discoveries and medical innovations by a nalyzing and modeling large-scale biomedical data\, especially single-cell genomics data. Jun had published ~30 papers in leading computational biol ogy journals such as genome research and cell stem cell. Currently\, Jun i s particularly interested in developing computational models and visualiza tions for cell differentiation and tumor microenvironment studies that eno rmously benefit from the emerging single-cell omics technologies.\n DTSTART:20210111T170000Z DTEND:20210111T180000Z LOCATION:CA\, QC SUMMARY:QLS Seminar Series - Jun Ding URL:/qls/channels/event/qls-seminar-series-jun-ding-33 5695 END:VEVENT END:VCALENDAR