About

Michael Snyder is a professor in Genetics at Stanford University and is associated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His work focuses on the intersection of genetics and artificial intelligence, contributing to advancements in medical imaging and healthcare. As a faculty member at Stanford, he is involved in research that leverages AI to improve medical diagnostics and treatment, and he plays a key role in the AIMI center's initiatives to integrate cutting-edge technology into healthcare applications.

Research topics

• Biology
• Genetics
• Computational biology
• Medicine
• Computer Science
• Internal medicine
• Evolutionary biology
• Bioinformatics
• Cell biology
• Pathology
• Psychology
• Endocrinology
• Artificial Intelligence
• Data science
• Engineering
• Machine Learning
• Sociology
• Biochemistry
• Social Science
• Political Science
• Neuroscience
• Physiology
• Physical therapy
• World Wide Web

Selected publications

• Leveraging health records to identify diagnoses associated with recurrent pregnancy loss across two medical centers

  iScience · 2026-01-07

  articleOpen access

  Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5-6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. In this study, we aim to identify diagnoses associated with RPL and generate hypotheses about RPL etiology utilizing electronic health record (EHR) data. We implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University EHR databases. In total, our study includes 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses are significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses have higher odds ratios for patients <35 years compared with 35+ years patients. While Stanford results are sensitive to control for healthcare utilization, UCSF results are stable across analyses with and without utilization.

  PublisherDOI

• In Depth Characterization of the Promoter Proximal Proteome of Single Copy Locus FOXP2

  Molecular & Cellular Proteomics · 2026-04-01

  articleOpen accessSenior author

  Identifying the proteins that interact with sequence-defined chromatin segments is a critical step in understanding gene expression. Most procedures perform bulk analysis of samples to identify the general interactions that occur in a cellular population and thereby do not detect the proteins that operate at a single locus. To circumvent this limitation, we developed a modified method that uses genetically targeted proximity labeling with dCas9-APEX2 to specifically biotinylate the promoter proximal proteome of the single copy locus FOXP2 in live HEK293 cells. After capture of the tagged proteins with streptavidin and isobaric labeling of the peptides produced from on-bead digestion with tandem mass tags, we used quantitative 2D-LC-MS3 on a tribrid mass spectrometer to identify 373 significantly enriched proteins at the active promoter relative to control samples (Storey-q<.05, FC>1.2). These proteins were enriched for transcription factors and components of the spliceosome. To validate our candidate transcriptional regulators, we utilized computationally predicted transcription factor binding and the >200 ChIP-Seq experiments performed in HEK293 cells by ENCODE. In addition to validating dozens of candidate transcription factors as binders of the targeted genomic locus, we newly identify IRF2BP2 and glucocorticoid signaling as negative regulators of FOXP2 transcription, suggesting they each play a key role in FOXP2 gene expression. We further demonstrate that MS detects approximately one third of both binders and non-binders, with more highly expressed genes significantly more likely to be detected regardless of binding status or locus specificity. ENCODE ChIP-Seq binders not detected by MS show significantly lower expression compared to non-binders only at the targeted FOXP2 promoter and not at off target loci.

  PublisherDOI

• A multidomain intrinsic capacity score tracks longitudinal health trajectories in the UK Biobank

  medRxiv · 2026-04-13

  articleOpen accessSenior authorCorresponding

  Abstract Quantitative measures for tracking functional health have generally been lacking. Intrinsic capacity (IC) has been proposed as an appropriate measure, but its metrics have been derived in small datasets and sparse longitudinal data. Using harmonized measures of cognition, locomotion, sensory function, vitality, and psychological well-being from 501,615 UK Biobank participants and followed for a median of 15.5 years, we derived domain-specific and composite IC scores. We examined associations with incident disease, cause-specific mortality, multimorbidity, lifestyle and socioeconomic factors, and multi-omic profiles from Olink proteomics, NMR metabolomics, clinical biochemistry, and blood-cell traits. We found that composite IC declined non-linearly with age, and within-person decline was steeper than the cross-sectional age measures. Participants with greater baseline morbidity, those who subsequently developed incident disease, and those who died earlier in follow-up showed lower IC trajectories across adulthood. The IC domains were only modestly correlated with one another, supporting multidimensionality, yet higher overall IC was associated with lower risk of most diseases examined. The dominant IC domain varied by endpoint, with cognition informative for dementia, sensory function for hearing loss, psychological capacity for depression, locomotion for osteoarthritis, and vitality for cardiometabolic outcomes. IC was also associated cross-sectionally with physical activity, insomnia, smoking, medication burden, and socioeconomic disadvantage. More proteins were found predictive for vitality, and enrichment converged on immune/inflammatory and metabolic pathways. Blood-based surrogates recapitulated part of the phenotypic signal, particularly for vitality. Overall, this IC framework captures longitudinal health trajectories and broad disease vulnerability in a large middle- to older-aged cohort and supports IC as a clinically meaningful, multidomain phenotype of aging and identifies blood-based correlates that may facilitate at-scale future monitoring of aging-related function declines.

  PublisherDOI

• An atlas of transcriptional dynamics in maternal blood over the course of healthy pregnancy

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-01

  articleOpen access

  Abstract Pregnancy results in profound physiological changes driven by dynamic and precisely programmed molecular processes. Maternal peripheral blood is generally the specimen of choice for studying these processes, as it is easily accessible and essential for many aspects of maintaining a healthy pregnancy. Here, we present a high-resolution atlas of the dynamic temporal changes in the transcriptome of maternal peripheral blood in healthy human pregnancy. We generated comprehensive RNA sequencing data in 802 weekly samples from 31 healthy pregnant women from the first trimester until after delivery. Using a strict discovery and replication setup, our longitudinal analysis of gene expression identified 720 genes with robust pregnancy-specific expression patterns. Using weighted graph correlation network analysis, we identified nine pregnancy-associated transcriptional modules that reveal a strong, coordinated enrichment of innate/neutrophil and antiviral immune programs, alongside changes in adaptive immunity (T cell differentiation and signaling), erythropoiesis and hemoglobin metabolism. Cell-type deconvolution revealed that these transcriptomic shifts were accompanied by increased relative neutrophil proportions and reduced naive CD4 and CD8 T cells in pregnancy. We provide a comprehensive characterization of dynamic changes across pregnancy, highlighting maternal blood as a key systemic regulator in healthy gestation. Together, our findings establish a reference atlas of healthy pregnancy, which can be used to identify dysregulated processes and mechanisms in women with pregnancy complications. Graphical abstract 720 genes showed robust pregnancy specific expression patterns. Co-expression analysis clustered the genes into nine modules with distinct dynamics. Enrichment in pathways involved in innate and neutrophil-mediated immunity, antiviral responses, T cell differentiation and signaling, erythropoiesis and hemoglobin metabolism. Cell-type deconvolution showed increases in neutrophils and decreases in naïve CD4 and CD8 T cells. The atlas of detailed longitudinal transcriptional changes provides a baseline reference for healthy pregnancy. Results for all genes and protein-protein interaction networks are made available for interactive exploration.

  PublisherOA PDFDOI

• Single-cell spatiotemporal dissection of the human maternal–fetal interface

  Nature · 2026-04-08

  articleOpen access

  Abstract The human maternal–fetal interface is characterized by mosaic intermingling of maternal and fetal cells 1 . Yet the underlying cellular, molecular and spatial programmes remain incompletely defined. Here we generate a comprehensive atlas of the human maternal–fetal interface across normal pregnancies from early gestation to term by integrating large-scale paired single-nucleus transcriptomic and chromatin accessibility profiling with submicrometre-resolution spatial transcriptomics and CODEX multiplex protein imaging 2 , substantially boosting the spatiotemporal resolution of prior research 3 . This framework delineates common and transient cell types, states and spatial niches across the fetal and maternal compartments, reconstructs transcriptional programmes that guide cytotrophoblast and decidual stromal cell differentiation, and resolves recurrent architecture structural units that build this interface. We identify previously unrecognized arterial endothelial state transitions during cytotrophoblast-mediated spiral artery remodelling and develop a machine learning model that predicts cytotrophoblast invasiveness from transcriptomic signatures. We further discover a decidual stromal cell subtype that suppresses cytotrophoblast invasion via endocannabinoid signalling at the human maternal–fetal interface. By integrating the atlas with genome-wide association data, we pinpoint maternal and fetal cells that are most vulnerable to pre-eclampsia, preterm birth or miscarriage. This resource provides a comprehensive spatially resolved single-cell multiomic reference of the human placenta and decidua and offers a framework for decoding their normal and disordered development.

  PublisherDOI

• Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report

  Journal of Diabetes Science and Technology · 2026-03-07

  articleOpen access

  A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

  PublisherDOI

• Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-03

  articleOpen access

  Regular physical activity represents one of the greatest mechanisms for maintaining human health, yet the underlying molecular transducers of these benefits remain incompletely understood. Multi-omic assays now provide new opportunities to study the coordinated molecular responses of body tissues to different exercise modalities. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this need by creating a molecular map of the response to physical activity. Described here is the first human cohort of MoTrPAC: sedentary adults enrolled prior to study suspension during the COVID-19 pandemic (N=175) randomized to either endurance or resistance exercise, or non-exercise control. From these participants, we detail their global acute molecular response in skeletal muscle, adipose tissue, and blood, integrated at multiple levels: tissue, exercise modality, timepoint, and omic category. These analyses characterize key molecular pathways, identify central regulators, and implicate novel candidate exerkines in mediating multi-organ exercise effects.

  PublisherDOI

• Quantification of domain-specific intrinsic capacity using mortality data

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-08

  articleOpen access

  Abstract Functional health is centered on five domains of Intrinsic Capacity (IC): locomotion, cognition, vitality, psychological and sensory capacity. Therefore, measuring IC at the domain-specific level is the cornerstone for developing preventive interventions to help individuals preserve their independence. In this study, we used 63 clinical features from the UK Biobank to develop ‘IC age’, an 18-year mortality risk estimator that approximates an individual’s biological age associated with the decline of each IC domain. By establishing proteomic surrogates of IC age, we find immune system activation across domains and provide a proteomic framework that may facilitate scalable monitoring of functional health decline.

  PublisherDOI

• Dynamic Longitudinal Multi-Omics Profiles in Developing Children with Overweight and Obesity and in Response to Behavioral Interventions

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes

  Journal of Diabetes Science and Technology · 2026-04-14

  articleOpen accessSenior authorCorresponding

  The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), β-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring (CGM) and wearable technologies enable a paradigm shift toward non-invasive, dynamic metabolic phenotyping. We show evidence that machine learning models can leverage high-resolution glucose data from at-home, CGM-enabled oral glucose tolerance tests to accurately predict gold-standard measures of muscle IR and β-cell function. This personalized characterization extends to real-world nutrition, where an individual's unique postprandial glycemic response (PPGR) to standardized meals, such as the relative glucose spike to potatoes versus grapes, could serve as a biomarker for their metabolic subtype. Moreover, integrating wearable data reveals that habitual diet, sleep, and physical activity patterns, particularly their timing, are uniquely associated with specific metabolic dysfunctions, informing precision lifestyle interventions. The efficacy of dietary mitigators in attenuating PPGR is also shown to be phenotype-dependent. Collectively, this evidence demonstrates that CGM can deconstruct the complexity of early dysglycemia into distinct, actionable subphenotypes. This approach moves beyond simple glycemic control, paving the way for targeted nutritional, behavioral, and pharmacological strategies tailored to an individual's core metabolic defects, thereby paving the way for a new era of precision diabetes prevention.

  PublisherOA PDFDOI

Recent grants

• Project 4

  NIH · $5.6M · 2019

• NIH Grant U54DE023789

  NIH · $500k · 2016

• Arabidopsis 2010: Development of an Arabidopsis Proteome Chip

  NSF · $1.8M · 2010–2012

• NIH Grant U54DK102556

  NIH · $5.4M · 2017

• Genomics of Gene Regulation in Progenitor to Differentiated Keratinocytes

  NIH · $7.0M · 2015–2017

Frequent coauthors

• Mark Gerstein

  286 shared

• Kévin Contrepois

  Stanford University

  155 shared

• Joel Rozowsky

  Lieber Institute for Brain Development

  123 shared

• Joseph C. Wu

  103 shared

• Sherman M. Weissman

  Yale University

  99 shared

• Alexander E. Urban

  Stanford University

  88 shared

• Anshul Kundaje

  Stanford University

  85 shared

• Ghia Euskirchen

  Stanford University

  78 shared

Education

• Ph.D., Genetics

  Stanford University

  1990

• M.D., Medicine

  Stanford University

  1985

• B.S., Biochemistry

  University of California, Berkeley

  1981