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DTSTART;TZID=America/New_York:20250403T160000
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DTSTAMP:20260419T033602
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SUMMARY:Fréchet Regression of Random Objects on Vector Covariates and Its Applications for  Single Cell RNA-seq Data Analysis
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday\, April 3\, 2025\n4:00pm\nHarvard TH Chan School of Public Health\, FXB G13 \nHongzhe Li\, PhD\nPerelman Professor of Biostatistics\, Epidemiology and Informatics\nDirector\, Center for Statistics in Big Data Vice Chair for Research Integration\, Department of Biostatistics\, Epidemiology and Informatics\, University of Pennsylvania \nPopulation-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the construction of cell-type- and individual-specific gene co-expression networks by estimating covariance matrices. Investigating how these co-expression networks relate to individual-level covariates provides critical insights into the interplay between molecular processes and biological or clinical traits. This talk introduces Fréchet regression\, modeling covariance matrices as outcomes and vector covariates as predictors\, using the Wasserstein distance between covariance matrices as a metric instead of the Euclidean distance. A test statistic is proposed based on the Fréchet mean and covariate-weighted Fréchet mean\, with its asymptotic null distribution derived. Analysis of large-scale single-cell RNA-seq data reveals an association between the co-expression network of genes in the nutrient-sensing pathway and age\, highlighting perturbations in gene co-expression networks with aging. Additionally\, a robust local Fréchet regression approach\, leveraging neural unbalanced optimal transport\, is briefly discussed to explore how cells are temporally organized during the differentiation of human embryonic stem cells into embryoid bodies.
URL:https://ds.dfci.harvard.edu/event/frechet-regression-of-random-objects-on-vector-covariates-and-its-applications-for-single-cell-rna-seq-data-analysis/
LOCATION:Harvard TH Chan School of Public Health\, 677 Huntington Ave\, Boston\, MA\, 02115
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20250415T160000
DTEND;TZID=America/New_York:20250415T170000
DTSTAMP:20260419T033602
CREATED:20250409T114825Z
LAST-MODIFIED:20250411T114358Z
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SUMMARY:Modeling Multiscale Genome and Cellular Organization
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Seminar\nTuesday April 15 at 4:00pm\nDana-Farber Cancer Institute\nCenter for Life Sciences Building\, 11th Floor\, Room 11081 \nJian Ma\, PhD\nRay and Stephanie Lane Professor of Computational Biology\nCarnegie Mellon University \n  \nThe intersection of Al/ML and biomedicine is entering a transformative era\, with growing potential to\nimpact both basic research and translational medicine. Yet\, despite remarkable advances in high-\nthroughput technologies across genomics and cell biology\, our understanding of the diverse cell types\nin the human body and the underlying principles of intracellular molecular organization and\nintercellular spatial interactions remains incomplete. A central challenge lies in developing\ncomputational frameworks that can integrate molecular\, cellular\, and tissue-level data to advance cell\nbiology at an unprecedented scale. In this talk\, I will present our recent work on machine learning\napproaches for regulatory genomics\, with a focus on single-cell 3D epigenomics. We introduce methods\nthat connect different layers of 3D genome architecture and cellular function at single-cell resolution\,\nincluding graph- and hypergraph-based models that capture spatial genome organization. I will also\nhighlight our latest efforts in developing self-supervised learning frameworks to delineate multiscale\ncellular interactions within complex tissues\, enabling the discovery of previously unrecognized spatially\norganized patterns. Together\, these Al-driven models provide a foundation for integrative\, multiscale\nrepresentations of cellular systems\, offering new insights into genome structure\, gene regulation\, and\ncell-cell communication. This line of work opens new opportunities toward building cohesive multiscale\ncellular models applicable across a broad range of contexts in health and disease.
URL:https://ds.dfci.harvard.edu/event/modeling-multiscale-genome-and-cellular-organization/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20250429T110000
DTEND;TZID=America/New_York:20250429T120000
DTSTAMP:20260419T033602
CREATED:20250417T114534Z
LAST-MODIFIED:20250417T114534Z
UID:6047-1745924400-1745928000@ds.dfci.harvard.edu
SUMMARY:Complex Disease Modeling And Efficient Drug Discovery With Large Language Models
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Seminar\nTuesday April 29 from 11:00-12:00pm\nZoom only (Link to be posted shortly) \nYu Li\, PhD\nAssistant Professor\, CSE\nThe Chinese University of Hong Kong \nLarge language models\, which can integrate and process large amounts of data in biomedicine\, have great potential in modeling complex diseases and discovering functional biomolecules for potential therapeutics. To model complex diseases and identify the potential drug targets for such diseases\, we built a language model trained on the insurance claims of around 123 million US people. With the model\, we can give a unified representation of all the common complex diseases\, which enables us to predict the genetic parameters of the diseases and discover unique genetic loci related to them efficiently. Then\, we developed models based on protein language models to efficiently discover remote homologs and functional biomolecules from nature\, such as signal peptides and antimicrobial peptides. With the model\, we can identify remote homologs 22 times faster than PSI-BLAST and discover diverse functional peptides with sequence similarity lower than 20% against the known ones. Finally\, we developed an RNA language model to model the RNA sequence and structure relation\, which enables us to perform RNA structure prediction and reverse design effectively. Within two months\, we designed and experimentally validated 19 RNA aptamers that are structurally similar\, yet sequence dissimilar\, to known light-up aptamers. More importantly\, 10 designed aptamers show higher fluorescence than the native Mango-I. The above projects demonstrate the great potential of large language models in promoting fundamental computational biological research and potential transformational development.
URL:https://ds.dfci.harvard.edu/event/complex-disease-modeling-and-efficient-drug-discovery-with-large-language-models/
CATEGORIES:Seminar
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