May ABC Seminar: Cong Ma, PhD – Univ of Michigan “Modeling and understanding the spatial tumor evolution”

May ABC Seminar: Cong Ma, PhD – Univ of Michigan “Modeling and understanding the spatial tumor evolution”

A tumor is a heterogeneous mixture of cancerous cells and multiple types of normal cells. Decoding this heterogeneity and identifying the genomic events that drive cancer development are key to studying tumor evolution and progression. Spatially resolved transcriptomics (SRT) technologies sequence expressed RNAs across thousands of locations in a tumor slice; the resulting gene expression signatures reveal the localization of cancer and adjacent normal cell types forming the tumor microenvironment. However, gene expression signatures alone are insufficient to identify cancer clones – subpopulations of cancerous cells that share the same genetic lineage – or to reconstruct the evolution of these clones. In this talk, I will introduce our recently published method, CalicoST, and our on-going improvements. CalicoST simultaneously infers allele-specific copy number aberrations (CNAs) and the spatial distribution of cancer clones using SRT data from one or more tumor slices. CalicoST identifies important types of CNAs – including copy-neutral loss of heterozygosity (LOH) and mirrored subclonal copy number aberrations – that are invisible to existing approaches. From these CNAs, CalicoST infers a phylogeny describing the ancestral relationships between the clones and revealing the spatial evolution of the tumor. Although CalicoST achieves state-of-the-art accuracy in inferring allele-specific CNAs and cancer clones, the optimization problem it tackles is challenging and prone to falling into local optima. To address this, we evaluated the challenges in clone inference using idealized simulations and developed a method to infer potential missing cancer clones for each CalicoST inference result.

Research Links: https://www.nature.com/articles/s41592-024-02438-9

Dr. Cong Ma is an assistant professor at the University of Michigan, having started this position last September. She earned her Ph.D. in computational biology from Carnegie Mellon University under the supervision of Dr. Carl Kingsford, and subsequently completed a postdoctoral fellowship at Princeton University in Dr. Ben Raphael’s group before joining the University of Michigan. Dr. Ma was awarded the prestigious Damon Runyon Quantitative Biology Fellowship, underscoring the significance of her work in developing computational methods to study spatial tumor evolution.

Speaker:  Cong Ma, PhD
Affiliation: University of Michigan Medical School
Position:  Assistant Professor of Computational Medicine and Bioinformatics
Host: Youn Kim, PhD, Gibson Lab

Date: Monday May 19th, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

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April ABC Seminar: Maria Carilli – Caltech – “Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data”

April ABC Seminar: Maria Carilli – Caltech – “Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data”

Transcription in individual cells is an inherently stochastic process, with non-deterministic numbers of RNA molecules produced per gene in transcriptional events across cells of the same cell type and condition. These distributions can be mathematical modeled according to biophysical hypotheses that specify system parameters of interest, such as rates of RNA splicing and degradation. While such stochastic models have been applied to infer RNA processing rates in fluorescence based assays, the scale of high-throughput sequencing data makes inference in this setting computationally challenging, especially as more molecular species, such as unspliced and spliced RNA molecules, can be quantified. We adapt an inference tool from machine learning, the variational autoencoder, that allows summarization and analysis of high-dimensional datasets to work with biophysical models of transcriptional dynamics. While preserving the variational autoencoder’s ability to reduce dimensionality of and denoise data, this work presents two major advances: one, principled integration and treatment of multimodal data (i.e., unspliced and spliced RNA molecules) through the employment of specific biophysical models; and two, inference of biophysical rates that parameterize those models, allowing the exploration of RNA dynamics across thousands of individual genes and cells.

Speaker:  Maria Carilli
Affiliation: California Institute of Technology
Position:  PhD Student, Lior Pachter Lab
Host: Dan MacDonald, PhD, Gibson Lab

Date: Monday April 28th, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Relevant Papers: Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data, Spectral neural approximations for models of transcriptional dynamics

 Maria Carilli is a graduate student at Caltech working on theory and software development for the analysis of single-cell RNA sequencing data. She obtained undergraduate degrees in biophysics and music from the University of Colorado Boulder with a minor in computational biology. She is particularly interested in combining tools from machine learning with mathematical modeling of biophysical dynamics for deeper, and scalable, understanding of cellular systems.

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February ABC Seminar: Prateek Prasanna, PhD – Stony Brook University – “Reality-aware Medical Vision: The Quest for Interpretable and Explainable AI in Medical Imaging”

February ABC Seminar: Prateek Prasanna, PhD – Stony Brook University – “Reality-aware Medical Vision: The Quest for Interpretable and Explainable AI in Medical Imaging”

Prateek Prasanna, PhDMedical imaging plays a critical role in modern healthcare, yet its complexity necessitates advanced computational tools to enhance analysis, interpretability, and diagnostic accuracy. In this presentation, we will discuss our research efforts in developing computational imaging biomarkers and frameworks for precision medicine in real clinical scenarios involving imperfect data. We will cover a spectrum of computational techniques grounded in both biological and domain-specific insights that facilitate the early detection and evaluation of treatment responses across various diseases.

In radiology, we have developed methods to analyze intricate tissue structures using radiomics and topology-informed deep learning, demonstrating improved prediction of treatment response in multiple cancers. Additionally, we explore techniques that integrate expert eye gaze patterns and radiomic features to condition diffusion models, enabling disease-aware image synthesis and enhancing the generation of anatomically accurate and clinically relevant medical images. These approaches provide both diagnostic accuracy and interpretability, bridging the gap between AI models and clinical needs.

In digital pathology, our work focuses on overcoming challenges such as domain shifts, data scarcity, and limited interpretability. We employ adaptive meta-learning frameworks to generalize across unseen staining patterns, improving segmentation and classification of histopathology images. Generative models are utilized to synthesize high-resolution pathology images and restore critical details, leveraging pathology text reports and handcrafted features to enhance both quality and interpretability. Finally, we integrate expert pathological insights with deep learning to improve interpretability in whole-slide image analysis, demonstrating the potential to provide meaningful explanations alongside robust performance.

These advancements highlight the transformative potential of explainable and generative AI in radiology and digital pathology, paving the way for innovative, clinically relevant solutions in medical imaging.

Speaker:  Prateek Prasanna, PhD
Affiliation: Stony Brook University
Position:  Assistant Professor, Department of Biomedical Informatics
Host: Daniel Shao, Mahmood Lab

Date: Monday February 24th, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Research Links: Publications, IMAGINE Lab

Prateek Prasanna is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University and directs the Imaging Informatics for Precision Medicine (IMAGINE) Lab. He received his PhD in Biomedical Engineering from Case Western Reserve University, Ohio, USA. Prior to that, he obtained his master’s degree in Electrical and Computer Engineering from Rutgers University and bachelor’s degree in Electrical and Electronics Engineering from National Institute of Technology, Calicut, India. Dr. Prasanna’s research focuses on building clinically translatable machine learning tools that leverage multiple data streams of imaging, pathology, and genomics to derive actionable insights for enabling better treatment decisions. His research involving development of companion diagnostic tools for thoracic, neuro, and breast imaging applications has been published in venues such as MICCAI, CVPR, ECCV, NeurIPS, Radiology, Medical Image Analysis, etc, and has won several innovation awards. One of the core focuses of his lab is to integrate machine generated inferences with expert clinical reads to make clinical workflows more efficient and effective. His team has been actively working on the advancement of interpretable machine learning and xAI techniques to facilitate the discovery of computational biomarkers, particularly in situations where data is limited or missing.

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January ABC Seminar: Stephen Pfohl, PhD – Google Research – “Algorithmic fairness and responsible AI for health equity”

January ABC Seminar: Stephen Pfohl, PhD – Google Research – “Algorithmic fairness and responsible AI for health equity”

Stephen PfohlIn this talk, I aim to present insights into the design of evaluations of machine learning and AI systems to assess properties related to algorithmic fairness and health equity. I argue that such evaluations are contextual and require specification of the intended use, target population, and assumptions regarding the data generating process and measurement mechanism. Through examples from my research, I argue that causal graphical models can serve as key tools for context specification and can aid in understanding and appropriate use of analytical algorithmic fairness techniques. I will further discuss recent work related to evaluation of equity-related biases in large language models.

Speaker:  Stephen Pfohl, PhD
Affiliation: Google Research
Position:  Senior Research Scientist
Host: Anurag Vaidya, Mahmood Lab

Date: Monday January 27, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Research Links: Personal siteGoogle Scholar

Stephen Pfohl is a senior research scientist at Google Research. His work focuses on the incorporation of fairness, distribution shift, and equity considerations into the design and evaluation of machine learning systems in healthcare contexts. Stephen earned his PhD in Biomedical Informatics from Stanford University.

November ABC Seminar: Brian Hie, PhD – Stanford University – “Sequence modeling from molecular to genome scale with Evo”

November ABC Seminar: Brian Hie, PhD – Stanford University – “Sequence modeling from molecular to genome scale with Evo”

The genome is a sequence that encodes the DNA, RNA, and proteins orchestrating an organism’s function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report the first scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA co-design with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genome scales of complexity, advancing our understanding and control of biology.

Title: Sequence modeling from molecular to genome scale with Evo
Speaker:  Brian Hie, PhD
Affiliation: Stanford University, Chemical Engineering and Data Science
Position:  Assistant Professor
Host: Jiening Zhu, PhD – Gerber Lab

Date: Monday November 18, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and an Innovation Investigator at Arc Institute, where his group conducts research at the intersection of biology and machine learning.

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Faisal Mahmood, PhD featured on Eric Topol’s “Ground Truths”

Faisal Mahmood, PhD featured on Eric Topol’s “Ground Truths”

Faisal Mahmood, Associate Professor of Pathology, was  featured on the July 28th, 2024 episode of Dr. Eric Topol’s Ground Truths podcast to discuss A.I.’s transformation of Pathology.  Ground Truths is a weekly podcast addressing facts, data, and analytics about biomedical matters.

 

October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

October ABC Seminar: Zhi Huang, PhD – Univ of Pennsylvania – “A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies”

The integration of Artificial Intelligence (AI) in clinical pathology has faced significant hurdles due to constraints in data collection and challenges associated with model transparency and interpretability.  In this talk, we introduce a novel digital pathology AI framework named nuclei.io, which leverages active learning and incorporates real-time feedback from human experts. This innovative approach empowers pathologists to quickly generate diverse datasets and develop models for various clinical applications. To demonstrate the effectiveness of our framework, we conducted two user studies employing such human–AI collaboration strategy. These studies focused on two key areas: the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. The results from these studies showed significant enhancements in sensitivity, accuracy, and diagnostic efficiency with the integration of AI. Our findings underscore the benefits of the human-in-the-loop AI framework, highlighting its potential to transform the field of digital pathology.

Speaker:  Zhi Huang, PhD
Affiliation: Perelman School of Medicine, University of Pennsylvania
Position:  Assistant Professor (incoming), Dept of Pathology and Laboratory Medicine, Dept of Biostatistics, Epidemiology and Informatics
Research Links: https://www.zhihuang.ai
Host: Andrew Song, PhD – Mahmood Lab

Date: Monday, October 21, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Zhi Huang is an incoming tenure-track assistant professor at the University of Pennsylvania starting January 2025. He obtained his PhD in Electrical and Computer Engineering (ECE) from Purdue University in August 2021. Since August 2021, He has been a postdoctoral fellow at Stanford University. His research focuses on AI/ML innovation and its application to medicine, with topics including vision-language foundation model for pathology, human-AI collaboration, neurodegenerative diseases, etc. His research has drawn wide public attention (including the New York Times, Stanford Magazine, and Stanford Scope) and has resulted in translational innovations. In 2022, Zhi Huang co-founded nuclei.io — a human-in-the-loop AI platform for digital pathology. It was selected as one of only 9 Stanford Catalyst 2023 cohort innovations.

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ABC Hybrid Seminar: Bokai Zhu, PhD – Ragon Institute – “Integration of spatial-omics and single-cell data across modalities with weakly linked features”

ABC Hybrid Seminar: Bokai Zhu, PhD – Ragon Institute – “Integration of spatial-omics and single-cell data across modalities with weakly linked features”

Bokai ZhuAdvancements in single-cell and spatial-omics technologies have created a need for integrating datasets across modalities with limited and weakly correlated features, such as those between spatial proteomics and transcriptomics. Existing tools, usually designed for strongly linked data, often fail in these scenarios. Recently, we have developed a series of methods (Mario and MaxFuse), that improves integration by refining weak correlations between modalities through an iterative smoothing and co-embedding process, and achieves single-cell level matching across these weakly linked modalities, enabling in-depth understanding of tissue micro-environments.

Speaker:  Bokai Zhu, PhD
Affiliation: Ragon Institute of MGH, Harvard and MIT, Broad Institute, MIT
Position:  Postdoctoral Research Fellow
Research Links: https://bokaizhu.github.io/
Host: Muhammad Shaban, PhD – Mahmood Lab

Date: Monday, September 23, 2024
Time: 1:00-2:00PM ET
In-person: Duncan Reid Conference Room (directions below)
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Bokai Zhu is currently a postdoctoral researcher supervised by Prof. Alex Shalek at the Ragon Institute at MGH, MIT, and Harvard. Prior to that, Dr. Zhu obtained his PhD in Microbiology and Immunology from Stanford University, under the supervision of Prof. Garry Nolan. He received a bachelor’s degree in Biology from Cornell University and Zhejiang University. Dr. Zhu’s doctoral research focused on: 1) Experimental assay development for multiplex imaging platforms; 2) Computational algorithm development for single-cell multi-omic integrations; 3)Application of the above tools in various biological systems.

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Dan MacDonald awarded Banting Fellowship

Dan MacDonald awarded Banting Fellowship

Daniel MacDonald, a Research Fellow in the Gibson Lab, is a 2024 recipient of the Banting Postdoctoral Fellowship.

The Banting Postdoctoral Fellowship program provides funding to the very best postdoctoral applicants, both nationally and internationally, who will positively contribute to the country’s economic, social and research-based growth. The award is designed for Canadian citizens, permanent residents of Canada and foreign citizens of Canada.

Dan is a second year fellow researching machine learning for the gut microbiome. Researchers typically study the microbiome by counting the different species of microbes in a stool sample—there may be hundreds to thousands of species and trillions of individual microbes. Stool samples are a good representation of the microbes found in the colon, but they underrepresent species in the small intestine and species that stick to the intestinal walls, which may play a critical role in maintaining the host’s bodily functions. Previously, researchers in the lab developed uncertainty-aware machine learning (ML) models of the gut microbiome that can predict how the hundreds-to-thousands of microbial species grow and die in response to changes in diet. These ML models were trained using stool samples, which underrepresent species upstream from the colon, and aren’t designed to accommodate other types of samples, such as microbe samples from tissue in the small intestine. In this research, Dan and other lab members are developing new uncertainty-aware ML models that will be trained not only on stool samples, but also using microbe measurements throughout the intestinal tract. This will provide insight into the as-yet unknown microbial interactions of the entire intestinal tract. They are designing this model to flexibly incorporate new measurement modalities, such as advanced imaging techniques, which will allow them to quickly adapt this model for new experiments in the ever-growing microbiome field, shedding light on the hidden inhabitants of our bodies.

I’m grateful to be a recipient of a 2024 Banting Postdoctoral Fellowship. This is shared achievement, as it was only possible with the support and countless opportunities provided by Dr. Travis Gibson and our team in the Division of Computational Pathology at BWH. I’d also like to express gratitude to my PhD supervisor, Prof. David Steinman, who laid the foundations for my academic pursuits and shaped my approach to research. It is truly an honour to receive this fellowship from NSERC, and I look forward to making meaningful scientific advancements through the completion of this research.  –Dan MacDonald

 

Mahmood Lab releases PathChat, a vision-language AI assistant for Pathology

Mahmood Lab releases PathChat, a vision-language AI assistant for Pathology

Developed by the Mahmood Lab, PathChat is a vision-language AI assistant for Pathology that can analyze histology images and answer diverse pathology-related queries.

See a demonstration of PathChat.

The PathChat publication can be found in the June edition of Nature here:  https://www.nature.com/articles/s41586-024-07618-3