By Kelly Peters,
Special to Rice News
As artificial intelligence plays an increasingly prominent role in decoding DNA, tracking pathogens and accelerating drug discovery, the line between real capability and hype can be unclear. Rice University experts can provide clear, technically grounded perspectives on how these tools are meaningfully advancing disease detection, public health preparedness and treatment design.
The AI2Health research cluster supported by Rice’s Ken Kennedy Institute brings together experts in computational biology, machine learning and systems biology to develop AI-powered solutions to critical challenges in human health and health governance. The AI2Health group is one of 12 research clusters within the Ken Kennedy Institute working to bridge departmental expertise and advance responsible AI and computing at Rice.
In addition to advancing foundational research, AI2Health members and associates focus on building practical, biologically inspired AI tools designed to make complex data easier to interpret and act on. Their methods can be applied across many areas of human health, and their expertise can help inform public dialogue and provide context on a number of topics, including:
Below are Rice experts who can offer background, help explain current research trends or answer questions in their areas of specialization:
Biosecurity and biosurveillance for public health
- Todd Treangen specializes in computational methods for pathogen surveillance to support public health initiatives like rapid outbreak response. His lab develops machine learning algorithms and open-source software that help scientists quickly identify harmful pathogens in synthetic DNA and metagenomic data with applications that meet emerging challenges in biosecurity and infectious disease monitoring. Treangen is the lead researcher for the AI2Health cluster.
Multi-omic methods for deciphering health and disease
- Vicky Yao develops machine learning and statistical approaches to analyze large, diverse biological datasets and extract meaningful insights. Her work prioritizes interpretability and data integration to uncover the molecular mechanisms underlying complex diseases such as cancer and Alzheimer’s.
AI and machine learning for genomics and metagenomics
- Santiago Segarra uses AI and advanced mathematical modeling to interpret complex biological data with a particular emphasis on graph machine learning methods for genomic and metagenomic datasets. His research provides foundational tools for understanding large-scale biological systems and the intricate networks that govern protein interactions, genetic organization and microbial ecology.
Computational biophysics for biomedical innovation
- Ivan Coluzza is a computational biophysicist using physics-based methods to study protein function and molecular design. His work integrates computation and theory to advance biomedical innovation, extending these models to design biomimetic materials inspired by the principles of protein folding.
Computational and systems biology for next-generation therapeutics
- Cameron Glasscock combines computational biology, protein design and synthetic biotechnology to engineer proteins with new or enhanced functions. His work informs next-generation therapeutics through physics-based and AI-enhanced modeling.
- Lydia Kavraki leverages her deep expertise in physical computing and robotics to advance computational methods for modeling protein flexibility and function. Her work creates innovative AI algorithms and software tools that accelerate drug discovery, enhance prediction of therapeutic efficacy and enable more precise design of personalized cancer immunotherapies.
Evolutionary biology
- Luay Nakhleh develops computational methods to study how genes, genomes and cellular networks evolve over time. His research helps shed light on the evolutionary processes that drive disease onset and progression with human-health relevant applications in areas such as cancer genomics.
Human genomics and structural variation in health and disease
- Fritz Sedlazeck develops next-generation AI and machine-learning methods to decode the full spectrum of human genomic variation. His research helps to improve diagnoses, personalize disease-risk prediction and uncover biological mechanisms underlying neurological, cardiovascular and developmental disorders.
“As a computational biologist, I think the field is at an interesting inflection point, and we can expect to see significant gains in the speed and scale at which we can analyze genomic data and uncover biological insights,” said Nakhleh, the William and Stephanie Sick Dean of Rice’s George R. Brown School of Engineering and Computing and professor of computer science and biosciences. “Continued collaboration and attention to the ethical dimensions of these tools will be essential going forward, and that commitment is at the core of the AI2Health research cluster.”
/Public Release. This material from the originating organization/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here.
