Bridging the gap between biological data and computational insight. Our work focuses on analyzing complex biological systems through algorithms, modeling, and data science.
Processing and analyzing genomic, transcriptomic, and proteomic datasets.
Algorithms for variant detection, alignment, and assembly.
Protein structure prediction and molecular dynamics simulations.
Pathway analysis and predictive models of cellular behavior.
Modern biology is increasingly data-driven. From high-throughput sequencing to mass spectrometry, the volume and complexity of data require robust computational approaches. We focus on the low-level research needed to handle this information effectively; building the tools and models that allow scientists to interpret biological phenomena.
Using machine learning for pattern recognition in large-scale datasets. Identifying disease markers, predicting gene expression levels, and analyzing variant effects.
Computational identification of drug targets and optimization of lead compounds. Ranking potential therapeutic candidates before experimental validation.
Leveraging deep learning for protein folding prediction. Running molecular simulations to understand protein movement and interactions at an atomic level.
Integrating patient-specific genomic data to tailor medical treatments. Analyzing variants to predict individual drug responses and disease susceptibility.
Modeling complex biological pathways and gene regulatory networks. Understanding how individual components interact to produce system-level behaviors.