Our Expertise and Leadership
Our outstanding faculty, staff, trainees and alumni have come to Colorado from around the world. Before joining our program, they trained at Harvard, Yale, Stanford, Johns Hopkins, and other top universities.
The Computational Bioscience core and affiliated faculty work in a wide range of areas, including biomedical text mining, protein structure simulations, RNA sequence and structure analysis, graphical models of protein interactions, and statistical analysis
of regulatory sequences. They have appointments in numerous departments on three CU campuses, including Medicine, Pharmacology, Biometrics, Biochemistry & Molecular Genetics, Computer Science, CCTSI; and we have faculty from National Jewish Health.
Research Areas Include:
- Biological Visualization Data
- Biomedical Ontology
- Clinical Research Informatics
- Computational Pharmacology
- Gene Expression
- Molecular Evolution
- Natural Language Processing
- Network Analysis
- Next Generation Sequence Analysis
- Personalized Medicine
- Statistical Methods in Genomics
- Text Mining
- Translational Bioinformatics
- Visual Analytics
- Physical simulations of biological macromolecules and their dynamics
Entity identification and normalization, information extraction, corpus linguistics, and computational lexical semantics, particularly in the molecular biology domain.
Systems and network biology approaches to disentangle signaling pathways in cancer development; Computational modeling of how therapeutic compounds function across different genomic backgrounds.
Application of machine learning, probabilistic models, and pattern recognition to large high throughput genomic datasets.
My research interests focus on the use of machine learning, multiple comparisons and multivariate statistical methods for the analysis of high-throughput data. I have focused most of my research on combining genomic data from either multiple datasets or from multiple studies. I have worked with transcriptomic, copy number, methylation and more recently, metabolomics and imaging data. Application areas I have worked in include cancer, diabetes, and ophthalmology. I have advised 12 Ph.D. students and one postdoctoral fellow in Biostatistics and Statistics on these topics. Students who would work with me in the future could expect to work on new analytical methods for large-scale datasets as well as begin to examine computational issues to enhance scalability.
Design, development, and evaluation of visualizations and visual analytics tools for supporting biologists in analyzing large and complex datasets. Also, application of text analytics research approaches to the biomedical literature.
Development and application of advanced computational techniques for biomedicine, particularly the application of statistical and knowledge–based techniques to the analysis of high–throughput data and of biomedical texts. Also, neurobiologically and evolutionarily informed computational models of cognition, and ethical issues related to computational bioscience.
Development and application of statistical methods for analyzing molecular sequences and high throughput genomic data.
Interests: Algorithms for human genome interpretation; Parallel and distributed architectures; Succinct data structures; Structural variation; Cancer genomics; Population genetics; Applications of genomics to clinical care
Research in the area of probabilistic graphical models and genomic data fusion, particularly for the tasks of gene prioritization, developing protein interaction networks and discovery of novel disease-gene associations.
Microbiology of the human gut and impacts on health. The development of bioinformatics techniques for analysis of marker gener and genomic sequence data.
Genome-enabled and bioinformatic approaches to studying microbial communities. Novel experimental methods and computational algorithms for assembly and analysis of high-throughput sequencing data.
Developing computational and statistical algorithms for analyzing microarray data, with special emphasis on time-course experimental design and alternative splicing detection. He works directly with scientists to translate biological concept into algorithm for rapid exploration and discovery. He is also interested in how best to share the vast bioinformatics and genomics resources between multi-disciplinary scientists.
Evolutionary genomics and molecular evolution, particularly the interaction between protein sequence, structure, function, and molecular co-evolution within and between proteins.
My research interests focus on developing and implementing systems genetics statistical models to complex traits/diseases.
Systems biology, genomics, and structural bioinformatics, particularly developing computational strategies to integrate genome sequence, gene expression, protein network, and protein structure information, with applications to respiratory diseases, including tuberculosis.
Computational phenotyping and data science using electronic medical records. Pharmacogenomics discovery, translation, and clinical implementation.
The Denman Lab's research is focused on elucidating the mechanisms of computation in the mammalian visual system. Towards this goal, the lab also develops neurotechnologies for recording from and modulating the mammalian brain at the spatial and temporal scales relevant to neural coding. To use these technologies, we develop movel algorithmic approaches to extracellular electrophysiology pre-processing, real-time data processing, and cross-modal image registration. We then apply these, and other, methods to studying distributed rodent visual system to attempt to understand the algorithm the brain is using to generate visual perception.
Translational bioinformatics, Personalization of progonsis and treatment plan, Heart failure/cardiomyopathy, Integration of public data resources,
We seek to describe the mechanisms that enable neural circuits to efficiently detect, amplify and transmit relevant information under diverse physiological conditions.
I have research experience in basic science and epidemiology of cardiac arrhythmias, with a current focus on applications of quantitative methods to clinical care and health. My ongoing projects are related to use of genetics and machine learning to guide clinical decision making, as well as smartphone applications for individualized medicine.
Professor Sikela’s research interests are in the development and application of advanced genome technologies, particularly as they apply to understanding of human evolution and human disease.