Artificial Intelligence in Bioinformatics & Analytics

Helping researchers use AI in ways that are accurate, secure, and useful for real biomedical data.

Artificial Intelligence is changing how research gets done, but most researchers do not have the time, infrastructure, or guardrails to use it effectively.

BARC can help researchers understand, evaluate, and apply AI tools within bioinformatics and analytics workflows while keeping data security, reproducibility, and research goals at the center.

Our Approach

We focus on practical AI adoption. That means helping researchers use AI where it adds value, avoiding unnecessary risk, and keeping human expertise in the loop.

Our goal is not to replace expert analysis. Our goal is to make research workflows faster, safer, more reproducible, and easier to understand.

Designing and Implementing AI

Our team can help your group implement AI and Machine Learning approaches for your data.

Agentic AI Development and Implementation
Consulting for using AI for complex bioinformatic/analytic needs
Developing and deploying machine learning models for your project
Optimization of existing AI to reduce compute cost

Our goal is to help you maximize the impact of AI and ML on your research.

Learn How to Use AI

We provide guidance and training to help researchers use AI tools effectively and responsibly.

Choosing the right AI tool for your project
Understanding local vs. cloud AI options
Improving prompting and token usage
Designing AI-assisted research workflows

Workshops and training sessions are currently in development.

AI Security & Policy Guidance

AI use in research creates real questions around privacy, security, compliance, and responsible data use.

Evaluating when AI is appropriate
Understanding local vs. cloud model risks
Protecting sensitive or unpublished data
AI-code audits
Aligning workflows with NIH, NSF, and institutional expectations

AI Nuggets of Knowledge

Thoughts, workflows, tutorials, and inspiration from the BARC staff
on the use of AI and Machine Learning in the Biomedical Sciences.

Practical AI workflows for bioinformatics and data analysis
Blog and vlog series from BARC staff
Using LLMs to accelerate coding, scripting, and troubleshooting
AI-assisted figure generation and scientific visualization
Prompt engineering for researchers and analysts
Understanding local vs. cloud-hosted AI models
Protecting sensitive, unpublished, and regulated data
Using AI responsibly in biomedical research
Aligning workflows with NIH, NSF, and institutional expectations
Reproducibility and validation of AI-assisted analyses
Common AI pitfalls, hallucinations, and verification strategies
AI-assisted literature review and hypothesis generation
Machine learning applications in genomics, imaging, and multiomics
Agentic AI and workflow automation for research support
Local AI tools for secure HPC and institutional environments
Open-source AI tools and emerging technologies
Real-world case studies from biomedical research projects
Future directions of AI in the biomedical sciences