The Computational Bioscience Program (CBP) of the University of Colorado School of Medicine is an independent Ph.D.-granting and postdoctoral training program. We have an innovative and highly productive approach to training pre- and post-doctoral fellows for research careers. We are a second-generation teaching program, informed by the experiences of the many computational biology training models that have come before us. Our program is designed to produce graduates with depth in both computational methods and molecular biology, an intimate familiarity with the science and technology that synthesizes the two, and the skills necessary to pioneer novel computational approaches to significant biomedical questions. We are aware of the difficulty of achieving both breadth and depth in a reasonable amount of time, and believe we have identified a novel approach that is capable of training productive interdisciplinary scientists in a relatively short period. The program is tightly focused on transforming already strong students and recent Ph.D.s into mature and productive scientists. Our program is structured around a set of four categories of educational goals and objectives: knowledge, communication skills, professional behavior and self-directed life-long learning. Our graduates demonstrate the knowledge of core concepts and principles of computational bioscience, and have the ability to apply computation to gain insight into significant biomedical problems. Their knowledge includes mastery of the fundamentals of biomedicine, statistics and computer science, as well as proficiency in the integration of these fields. Our graduates will contribute to the discovery and dissemination of new knowledge. Our graduates demonstrate interpersonal, oral and written skills that enable them to interact productively with scientists from both biomedical and computational domains, to clearly communicate the results of their work in appropriate formats, and to teach others computational bioscience skills. Our graduates are able to bridge the gap between biomedical and computational cultures. Our graduates demonstrate the highest standards of professional integrity and exemplary behavior, as reflected by a commitment to the ethical conduct of research, continuous professional development, and thoughtfulness regarding the broader implications of their work. Our graduates demonstrate habits and skills for self-directed and life-long learning, and recognize that computational bioscience is a rapidly evolving discipline. Our focus is on the development of adaptive, flexible and curious scientists able to comfortably assimilate new ideas and technologies during the course of their professional development.
| Callahan, Tiffany J; Baumgartner, William A; Bada, Michael et al. (2018) OWL-NETS: Transforming OWL Representations for Improved Network Inference. Pac Symp Biocomput 23:133-144 |
| Powers, Rani K; Goodspeed, Andrew; Pielke-Lombardo, Harrison et al. (2018) GSEA-InContext: identifying novel and common patterns in expression experiments. Bioinformatics 34:i555-i564 |
| Boguslav, Mayla; Cohen, K Bretonnel; Baumgartner, William A et al. (2018) Improving precision in concept normalization. Pac Symp Biocomput 23:566-577 |
| Tripodi, Ignacio J; Allen, Mary A; Dowell, Robin D (2018) Detecting Differential Transcription Factor Activity from ATAC-Seq Data. Molecules 23: |
| Ross, Brian C; Boguslav, Mayla; Weeks, Holly et al. (2018) Simulating heterogeneous populations using Boolean models. BMC Syst Biol 12:64 |
| Russell, Pamela H; Vestal, Brian; Shi, Wen et al. (2018) miR-MaGiC improves quantification accuracy for small RNA-seq. BMC Res Notes 11:296 |
| Rudra, Pratyaydipta; Shi, Wen J; Russell, Pamela et al. (2018) Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse. BMC Genomics 19:639 |
| Lipner, Ettie M; Knox, David; French, Joshua et al. (2017) A Geospatial Epidemiologic Analysis of Nontuberculous Mycobacterial Infection: An Ecological Study in Colorado. Ann Am Thorac Soc 14:1523-1532 |
| Azofeifa, Joseph G; Allen, Mary A; Lladser, Manuel E et al. (2017) An Annotation Agnostic Algorithm for Detecting Nascent RNA Transcripts in GRO-Seq. IEEE/ACM Trans Comput Biol Bioinform 14:1070-1081 |
| Rudra, Pratyaydipta; Shi, W Jenny; Vestal, Brian et al. (2017) Model based heritability scores for high-throughput sequencing data. BMC Bioinformatics 18:143 |
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