David Fischer
@davidsebfischer
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I develop mechanistic machine learning tools for single-cell and spatial omics data to understand the regulatory patterns underlying human disease dynamics.
Cambridge, MA
Joined September 2014
I’m thrilled to announce that I will join the Institute of Artificial Intelligence at the Medical University of Vienna (@MedUni_Wien), led by @BockLab, as a tenure-track assistant professor starting December 2024! Check out our website and open positions
ai4biomedicine.org
Academic lab for mechanistic machine learning and AI models of cells. We focus on cell biology captured by single-cell and spatial omics.
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RT @NatureRevGenet: Our featured article: Adapting systems biology to address the complexity of human disease in the single-cell era https:….
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RT @Schmidt_Center: A shift in perspective: #systemsbiology and single-cell genomics are transforming how we study disease. Learn more in a….
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This review is the product of a great team effort together with Martin Villanueva, Peter Winter @peterswinter and Alex Shalek @shaleklab! &
nature.com
Nature Reviews Genetics - Differences between humans and experimental models create a translational gap that makes it difficult to extrapolate research findings. The authors review systems-focused...
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In summary, we outline how systems biology is being adapted to the multiscale dynamics of human health and disease in omics-driven as what is effectively a two-loop cycle over discovery and validation.
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We review strategies that can manage this distance and dissect how it relates to understanding cellular systems at specific spatiotemporal scales - e.g. the cellular scale often considered in single-cell-resolved experiments or the tissue niche scale in spatial omics experiments.
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However, the usage of two distinct systems incurs a "translational distance" that complicates systems biology approaches that utilize information from the two.
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We leverage that insight to describe how one can translate between discovery efforts in human tissues and validation efforts in experimental model systems. Both are needed to build quantitative models are faithful to human biology and validated through perturbation experiments.
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In this review, we discuss how one can rationalize what information about a multiscale cellular system is actually captured by on omics study.
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This obstructs attempts at building quantitative models of cellular systems.
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On can trace some of these ideas back to the early days of systems biology. However, the dynamics of human tissues in disease settings is multiscale - not only does that impact quantitative models, it also reflects in experimental design and publicly available data resources.
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Currently, there's a lot of interest in quantitative models that would help us understand and predict features of the complex cellular systems that underlie human health and disease - think about virtual cells, for example.
New online! Adapting systems biology to address the complexity of human disease in the single-cell era
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RT @TyMillerLab: Proud to share our latest work in @nature, co-led by @chadi_elfarran, @CouturierMDPhD & @BradEBernstein! We developed a ne….
nature.com
Nature - A study of myeloid cells in gliomas, a type of brain tumour, used a factor-based computational framework to reveal four immunomodulatory gene-expression programs that are expressed across...
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Read about self-supervision in models of scRNA-seq data in this deep dive!.
Our paper on Self-Supervised Learning in Single-Cell Genomics is out in @NatMachIntell! 🙌.Huge thanks to my co-authors @mojtabahrami, Yufan Xia, @davidsebfischer , and @fabian_theis 👏.
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RT @CanAztekin: 🎉Excited to announce job openings in our new lab at @MPI_Bio /FML in Tübingen!🇩🇪Looking for enthusiastic colleagues to expl….
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What cellular & multicellular changes underpin aging of ovaries? We used spatial transcriptomics to study this process in mice, capturing dynamic changes in cells, follicles, and tissue across the estrous cycle. Read about the age-related changes in these dynamics we discovered!.
Excited to share our lab's first preprint: we used spatial transcriptomics to dissect how aging disrupts the cycling ovary! 🧵. This was an incredible team effort w/ @jenngarrison @Tammyinlab @davidsebfischer AlisonKochersberger @RuthRaichur @sophiamszady
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RT @mariabrbic: 🚀 We're #Hiring #PhD students at MLBio Lab at @EPFL_en through the EDIC Program!.📅 Deadline: Dec 15. Are you passionate abo….
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Join our ML challenge on spatial transcriptomics!.
Important update on our #MLchallenge – the dataset for Crunch 2 has been released! Crunch 2 focuses on leveraging cell transcriptional profiles to enhance the predictive model’s ability to infer the expression of unknown genes in spatial contexts:
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RT @NatureRevGenet: Slide-tags enables spatial single-nucleus sequencing.#ToolsOfTheTrade by Andrew J. C. Russell @….
nature.com
Nature Reviews Genetics - In this Tools of the Trade article, Andrew Russell describes Slide-tags, a method for scalable, spatially resolved single-nucleus sequencing.
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RT @BockLab: 🧑💻With CellWhisperer, your personal single-cell RNA-seq expert and assistant is waiting to talk to you!.• Watch a short demo….
biorxiv.org
Single-cell RNA-seq characterizes biological samples at unprecedented scale and detail, but data interpretation remains challenging. Here we introduce CellWhisperer, a multimodal machine learning...
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RT @MDLuecken: Are you interested in sc-spatial multiomics data, motivated by improving patient outcomes in lung cancer, and keen to join a….
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