AI in Molecular Biology1
Sep 15–18, 2025 | Eldorado Hotel & Spa, Santa Fe, NM, United States
Scientific Organizers:
David R. Kelley and Jean Fan
In Person
On Demand
×
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Sep 15–18, 2025 | Eldorado Hotel & Spa, Santa Fe, NM, United States
Scientific Organizers: David R. Kelley and Jean Fan
David R. Kelley
Jean Fan
Available Formats:
Supported by the Directors' Fund
In Person
On Demand
***Meeting program subject to change.
Available Formats: = In Person = On DemandMonday, September 15, 2025
Fundraising
Booking Function
Merchandise Options
Registration Options
Registration
4:00–8:00 PM
Concourse
Welcome Mixer
6:00–8:00 PM
Cava Santa Fe
Tuesday, September 16, 2025
Breakfast
7:00–8:00 AM
Anasazi Ballroom
Poster Setup
7:30–8:00 AM
Anasazi Ballroom
Poster Viewing
7:30–5:00 PM
Anasazi Ballroom
Welcome and Keynote Address
8:00–9:00 AM
Eldorado Ballroom
Barbara Engelhardt, Stanford University School of Medicine and Gladstone Institutes
Machine Learning Methods for Live-Cell Imaging Cell Behavioral Analyses
Machine Learning Methods for Live-Cell Imaging Cell Behavioral Analyses
Extracting Interpretable Insights from Biological AI
9:00–11:30 AM
Eldorado Ballroom
Maria Brbic, EPFL
Tissue Reassembly with Generative AI
Tissue Reassembly with Generative AI
Jian Ma, Carnegie Mellon University
Learning Multiscale Cellular Organization and Interactions
Learning Multiscale Cellular Organization and Interactions
Dmitry Kobak, Tübingen University
Self-Supervised and Unsupervised Learning for 2D Visualization of Scientific Datasets
Self-Supervised and Unsupervised Learning for 2D Visualization of Scientific Datasets
Emma Pierson, University of California, Berkeley
Equitable Machine Learning in Healthcare
Equitable Machine Learning in Healthcare
Coffee Break
9:30–9:50 AM
Concourse
Lunch
11:30–12:30 PM
Anasazi Ballroom
Poster Session 1
12:00–2:30 PM
Anasazi Ballroom
Symposia Spotlight 1: Late-breaking research presentations selected from abstract submissions
2:30–4:30 PM
Eldorado Ballroom
Miten Jain, Northeastern University
Deep Learning and Nanopore Technology Reveal Single-Molecule Insights Into DNA, RNA, and Protein Modifications
Deep Learning and Nanopore Technology Reveal Single-Molecule Insights Into DNA, RNA, and Protein Modifications
Evan Seitz, Cold Spring Harbor Laboratory
Mapping the Mechanistic Impact of Genetic Variation with Interpretable Deep Learning
Mapping the Mechanistic Impact of Genetic Variation with Interpretable Deep Learning
Manu Saraswat, German Cancer Research Centre
Dissecting Cellular Plasticity in Glioblastoma via Deep Learning of Single-Cell Gene Regulatory Networks
Dissecting Cellular Plasticity in Glioblastoma via Deep Learning of Single-Cell Gene Regulatory Networks
Ryan Keivanfar, UC Berkeley + UCSF Gladstone Institutes
Allele-Specific Expression Prediction from Personal Genome Sequences Using Fine-Tuned Enformer Models
Allele-Specific Expression Prediction from Personal Genome Sequences Using Fine-Tuned Enformer Models
Abdul Muntakim Rafi, University of British Columbia
Detecting and Avoiding Homology-Based Data Leakage in Genome-Trained Sequence Models
Detecting and Avoiding Homology-Based Data Leakage in Genome-Trained Sequence Models
Alexander S Garruss, Stowers Institute for Medical Research
Synergistic Control of Gene Expression by 5’ UTR and Synonymous Codon Variants
Synergistic Control of Gene Expression by 5’ UTR and Synonymous Codon Variants
Ofir Yaish, Ben-Gurion University of the Negev
Fast and Interpretable Motif Discovery from Attribution Maps via Fourier-Based Clustering
Fast and Interpretable Motif Discovery from Attribution Maps via Fourier-Based Clustering
Yijie Kang, Stony Brook University; Cold Spring Harbor Laboratory
Decoding the Sequence Basis of Pol II Elongation with Deep Learning
Decoding the Sequence Basis of Pol II Elongation with Deep Learning
Coffee Available
4:30–5:00 PM
Concourse
Parsing and Annotating the Noncoding Genome
5:00–7:00 PM
Eldorado Ballroom
David R. Kelley, Calico Life Sciences
Predicting Gene Expression from DNA Sequence
Predicting Gene Expression from DNA Sequence
Stein Aerts, Katholieke Universiteit Leuven
Single-Cell Multiomic Inference of Gene Regulatory Networks
Single-Cell Multiomic Inference of Gene Regulatory Networks
Christina S. Leslie, Memorial Sloan-Kettering Cancer Center
Machine Learning for Single Cell and Regulatory Genomics in Cancer
Machine Learning for Single Cell and Regulatory Genomics in Cancer
Sahin Naqvi, Boston Children's Hospital
Short Talk: Deep Learning Prediction of The Chromatin Response to Transcription Factor Dosage from DNA Sequence
Short Talk: Deep Learning Prediction of The Chromatin Response to Transcription Factor Dosage from DNA Sequence
McKayla Ford, University of Rochester
Short Talk: Secondary Structures as Key Regulators of Transcription at CpG island Promoters
Short Talk: Secondary Structures as Key Regulators of Transcription at CpG island Promoters
On Own for Dinner
7:00–8:00 PM
Wednesday, September 17, 2025
Breakfast
7:00–8:00 AM
Anasazi Ballroom
Mapping Cellular Interactions in Tissues with Spatial Imaging and Genomics
8:00–11:00 AM
Eldorado Ballroom
Jean Fan, Johns Hopkins University
Machine Learning for Spatial Omics Data Integration
Machine Learning for Spatial Omics Data Integration
Mingyao Li, University of Pennsylvania School of Medicine
Unlocking the Power of Spatial Omics with AI
Unlocking the Power of Spatial Omics with AI
Faisal Mahmood, Brigham and Women's Hospital
Multimodal, Generative, and Agentic AI for Pathology
Multimodal, Generative, and Agentic AI for Pathology
Joshua Welch, University of Michigan
Modeling Cell Fate Transition in Space and Time with Graph Neural Networks
Modeling Cell Fate Transition in Space and Time with Graph Neural Networks
Juanru Guo, Washington University in St. Louis School of Medicine
Short Talk: A Machine Learning Approach to Soft Segmentation and Manifold Unrolling of Visium HD Data
Short Talk: A Machine Learning Approach to Soft Segmentation and Manifold Unrolling of Visium HD Data
Christine Y Yeh, Stanford University
Short Talk: Robust Self-Supervised Machine Learning for Single Cell Embeddings and Annotations
Short Talk: Robust Self-Supervised Machine Learning for Single Cell Embeddings and Annotations
Coffee Break
9:00–9:20 AM
Concourse
Poster Setup
11:00–1:00 PM
Anasazi Ballroom
On Own for Lunch
11:00–1:30 PM
Poster Viewing
1:00–10:00 PM
Anasazi Ballroom
Career Roundtable
1:30–2:30 PM
Eldorado Ballroom
Panel Discussion: Challenges and Opportunities for Innovation and Responsibility in AI-Driven Biology with Academic and Industry Leaders
2:45–4:30 PM
Eldorado Ballroom
* Jean Fan, Johns Hopkins University
Coffee Available
4:30–5:00 PM
Concourse
Learning a Language of Cellular Morphology from Imaging
5:00–7:00 PM
Eldorado Ballroom
Berton Earnshaw, Recursion Pharmaceuticals
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
David Van Valen, Caltech
Cell Segmentation Foundation Models
Cell Segmentation Foundation Models
Anita Donlic, Princeton University
Short Talk: Deep Learning-Driven Profiling of Biomolecular Condensate Images Reveals Mesoscale Structure-Molecular Function Relationships
Short Talk: Deep Learning-Driven Profiling of Biomolecular Condensate Images Reveals Mesoscale Structure-Molecular Function Relationships
Lucy Luo, Northwestern University
Short Talk: Spatially-Restricted Circuits Between CXCL9+ Macrophages and T Cells Drive Acute Cellular Rejection in Lung Transplant Patients
Short Talk: Spatially-Restricted Circuits Between CXCL9+ Macrophages and T Cells Drive Acute Cellular Rejection in Lung Transplant Patients
Sehyun Oh, The City University of New York
Short Talk: A Multi-Modal Resource for Integrating Histopathology with Multi-Omics Data in R/Bioconductor
Short Talk: A Multi-Modal Resource for Integrating Histopathology with Multi-Omics Data in R/Bioconductor
Wei Li, University of Pennsylvania
Short Talk: A Virtual Machine for Multimodal Spatial Omics
Short Talk: A Virtual Machine for Multimodal Spatial Omics
Social Hour with Dinner
7:00–8:00 PM
Anasazi Ballroom
Poster Session 2
7:30–10:00 PM
Anasazi Ballroom
Thursday, September 18, 2025
Breakfast
7:00–8:00 AM
Anasazi Ballroom
Toward an Integration of AI and Systems Biology
8:00–11:00 AM
Eldorado Ballroom
Jacob Kimmel, NewLimit
Learning the Combinatorial Code of Epigenetic Reprogramming
Learning the Combinatorial Code of Epigenetic Reprogramming
Hector Garcia Martin, Lawrence Berkeley National Laboratory
Bioengineering Cells to Produce Biofuels and Renewable Biomaterials
Bioengineering Cells to Produce Biofuels and Renewable Biomaterials
Shyam Prabhakar, Agency for Science, Technology and Research (A*STAR)
We Are Not All the Same: Early Insights into Human Diversity from Single Cell and spatial Omics
We Are Not All the Same: Early Insights into Human Diversity from Single Cell and spatial Omics
Angela R Wu, Hong Kong University of Science and Technology
UniGeneX Creates a Universal Gene Expression Single-Cell Atlas and Uncovers Key Transitional Pathological Cell States
UniGeneX Creates a Universal Gene Expression Single-Cell Atlas and Uncovers Key Transitional Pathological Cell States
Reet Mishra, UC Berkeley-UCSF
Short Talk: Decoding Microglial State Dynamics via CRISPR Perturbations and Multi-Modal Generative Single-Cell Modeling
Short Talk: Decoding Microglial State Dynamics via CRISPR Perturbations and Multi-Modal Generative Single-Cell Modeling
Akanksha Sachan, University of Pittsburgh
Short Talk: Uncovering Dynamic Regulatory Circuits Underlying Bifurcating Human B Cell States
Short Talk: Uncovering Dynamic Regulatory Circuits Underlying Bifurcating Human B Cell States
Coffee Break
9:00–9:20 AM
Concourse
Panel Discussion: Complementarity of Industry and Academia
11:00–12:00 PM
Eldorado Ballroom
On Own for Lunch
11:00–2:30 PM
Symposia Spotlight 2: Late-breaking research presentations selected from abstract submissions
2:30–4:30 PM
Eldorado Ballroom
Suryanarayana Maddu, Simons Foundation
Scalable Inference of Biophysical Models from Multi-Omics Data
Scalable Inference of Biophysical Models from Multi-Omics Data
Mehrshad Sadria, Altos Labs
FateNet: an Integration of Dynamical Systems and Deep Learning for Cell Fate Prediction
FateNet: an Integration of Dynamical Systems and Deep Learning for Cell Fate Prediction
Jishnu Das, University of Pittsburgh
Sliding Window INteraction Grammar (SWING): A Generalized Interaction Language Model for Peptide and Protein Interactions
Sliding Window INteraction Grammar (SWING): A Generalized Interaction Language Model for Peptide and Protein Interactions
Adela Habib, Los Alamos National Laboratory
Harnessing Coupled Protein Language Model and Geometric Deep Learning for Protein Interaction Prediction on Dynamically Sampled Surfaces
Harnessing Coupled Protein Language Model and Geometric Deep Learning for Protein Interaction Prediction on Dynamically Sampled Surfaces
Yuanhao Qu, Stanford University
AutoScreen: an AI Scientist System for Target Discovery in Functional Genomics
AutoScreen: an AI Scientist System for Target Discovery in Functional Genomics
John-William Sidhom, Weill Cornell Mediine
Learning the Language of Somatic Mutations: A Large Language Model Approach to Precision Oncology
Learning the Language of Somatic Mutations: A Large Language Model Approach to Precision Oncology
Nicholas Hutchins, Massachusetts Institute of Technology
Reconstructing Signaling Histories of Single Cells Via Perturbation Screens and Transfer Learning
Reconstructing Signaling Histories of Single Cells Via Perturbation Screens and Transfer Learning
Nathaniel Robichaud, Nomic Bio
Large-scale Proteomics for AI/ML Applications: Insights from Quantifying 1,000 Proteins In 20,000 Samples
Large-scale Proteomics for AI/ML Applications: Insights from Quantifying 1,000 Proteins In 20,000 Samples
Coffee Available
4:30–5:00 PM
Concourse
Analyzing and Harnessing Proteins
5:00–6:30 PM
Eldorado Ballroom
Mona Singh, Princeton University
Protein Language Models: Strengths and Limitations
Protein Language Models: Strengths and Limitations
Ava Amini, Microsoft Research
Generative AI for Protein Sequence Design
Generative AI for Protein Sequence Design
Henry R Kilgore, Whitehead Institute for Biomedical Research
Short Talk: Protein Codes Promote Selective Subcellular Compartmentalization
Short Talk: Protein Codes Promote Selective Subcellular Compartmentalization
Julia Rogers, Columbia University
Short Talk: Machine Learning the Binding Affinities of Protein–Peptide Interactions in Cell Signaling
Short Talk: Machine Learning the Binding Affinities of Protein–Peptide Interactions in Cell Signaling
Closing Keynote Address
6:30–7:15 PM
Eldorado Ballroom
David Baker, University of Washington
Remote Presentation: Protein Design using Deep Learning
Remote Presentation: Protein Design using Deep Learning
Meeting Wrap-Up: Outcomes and Future Directions (Organizers)
7:15–7:30 PM
Eldorado Ballroom
Social Hour with Dinner
7:30–8:30 PM
Anasazi Ballroom
Entertainment
8:00–11:00 PM
Eldorado Ballroom
Cash Bar
8:00–11:00 PM
Anasazi Ballroom
Friday, September 19, 2025
Departure
12:00–11:59 PM
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