Perturbation prediction at single-cell resolution
We are building generative AI models trained on millions of single-cell profiles to predict the effects of perturbations across tumor and microenvironmental cell states.
AI-guided brain tumor research
Using AI and single-cell genomics to discover better therapies for brain tumors.
The lab develops computational methods and data resources to advance the understanding of brain tumor biology and enable predictive modeling, mechanistic insight, and clinically actionable discovery.
Generative AI models
Predicting cellular responses to perturbations across brain tumor states
Single-cell atlases
Millions of profiles spanning malignant, immune, and neural ecosystems
Therapeutic prioritization
Drug repurposing and target discovery grounded in patient-specific data
- Generative AI for perturbation and response modeling
- Single-cell and multimodal genomics of brain tumors
- Drug repurposing using molecular and chemical structure data
- Translational models for patient-specific therapeutic hypotheses
Current directions
The lab is focused on computational tools and data resources that can make brain tumor biology more predictive, mechanistic, and clinically actionable.
Drug repurposing for brain tumors
We combine large-scale single-cell datasets with chemical structure information to identify therapeutic candidates that can be repurposed for brain tumors.
Lab-built methods and resources
Representative tools from the program include SCRAM, COORS, CLIPPR, CaSpER, and XCVATR, spanning cell annotation, developmental state mapping, subtyping, and RNA-inferred genomic profiling.
Browse methods →AI-guided therapeutic discovery
Connecting cell states, perturbation models, and drug repurposing.
Our computational framework integrates large-scale single-cell tumor data, predictive AI models, and chemical representations to identify therapies that matter for brain tumors.
Akdes Serin Harmanci
Assistant Professor, Neurosurgery
Akdes Serin Harmanci leads the Serin Lab at Baylor College of Medicine. The lab develops AI and single-cell genomic methods to study brain tumors, model therapeutic response, and translate molecular complexity into tractable treatment strategies.
Join the lab
We are interested in students, postdocs, and collaborators who want to work on AI, machine learning, single-cell analysis, cancer genomics, and computational neuro-oncology.
If you are reaching out about a position, include your CV, a short summary of your background, and a few sentences about the problems you want to work on.
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