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Program Disclaimer:Within the next academic year, the Division of Biostatistics and Bioinformatics will merge into the newly named Department of Biostatistics, Health Informatics, and Data Sciences (BHIDS) within the College of Medicine. This will create a better platform for faculty and students in the Data Sciences to further their research and research opportunities.
The Master of Science and Doctoral programs in Environmental Health-Biostatistics will also merge into BHIDS and undergo a name change to represent the program's new home better. Future admitted students will be eligible to obtain their degree with the new name if they choose.
Housed in UC’s department of Environmental and Public Health Sciences with collaborations throughout the College of Medicine and Cincinnati Children’s Hospital Medical Center, the Division of Biostatistics and Bioinformatics offers exciting training and research opportunities in biomedical data sciences. Training programs in the division combine rigorous statistical and computational education rooted in the probability theory and computer science with the exposure to a broad range of biomedical research applications.
Biostatistics is a data science field concerned with application of statistical reasoning in the biomedical and public health research. Biostatisticians develop statistical methodologies that are tailored to address specific biomedical data analysis problems. Biostatisticians are also members of interdisciplinary biomedical research teams whose role is to ensure optimal use of data to answer specific biomedical research questions.
Bioinformatics is an interdisciplinary field that develops methods and computational tools for understanding high-dimensional biomedical data. Bioinformatics combines computer science, statistics, mathematics, and engineering to manage, process and analyze biomedical data. There and many overlaps between Biostatistics and Bioinformatics in terms of methodologies utilized and domains of application in biomedical research. However, Bioinformatics tends to be more focused on the analysis and interpretation of high dimensional datasets such as genomics, proteomics and metabolomics. Furthermore, Bioinformatics research objectives often involve development of software tools that facilitates management and analysis of large and complex datasets.
Both Biostatistics and Bioinformatics are integral parts of the new emerging field of Biomedical Data Sciences. Data Sciences in general is a field dedicated to extraction knowledge from data. In the context of the biomedical and public health research, data sciences integrate traditional statistical reasoning with the technological and computational solutions needed to organize, integrate and analyze relevant data. The biomedical and public health research is increasingly becoming data-intensive and data-driven. The challenges and opportunities offered by accessing, managing, analyzing, and integrating datasets of diverse data types (exposure, health, behavioral, genomics, genetics, etc) is captured by the term “Big Data”. The graduate programs and research within our division reflect rapidly increasing role that the data sciences play in contemporary biomedical and public health research.
Current methodological research undertaken by division faculty includes statistical methods for multiple hypothesis testing, statistical genetics, supervised and unsupervised Bayesian and machine learning methods for genomics data analysis, methods for next generation sequencing data analysis, statistical geospatial modeling, integrative statistical models for Big Data, computational drug screening, and protein structure modeling.
A few examples of interdisciplinary biomedical research projects that involve division faculty are study of predictive transcriptional signature for juvenile idiopathic arthritis, genomic determinants of kidney cancer, cancer treatment clinical trials, and numerous biomedical projects investigating gene-environment interactions.
Li, Yiming; Hsu, Wei-Wen (2022. ) A classification for complex imbalanced data in disease screening and early diagnosis. Statistics in medicine, , 41 (19 ) ,3679-3695More Information
Hsu, Wei-Wen; Mawella, Nadeesha R; Todem, David (2022. ) On testing for homogeneity with zero-inflated models through the lens of model misspecification.International statistical review = Revue internationale de statistique, , 90 (1 ) ,62-77More Information
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Day: FridayTime: 1 to 2 p.m.Location: Kettering Lab Rm. G17For more information, please email ehgrad@ucmail.uc.edu
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Department of Environmental & Public Health SciencesDivision of Biostatistics and BioinformaticsKettering Lab BuildingRoom 133160 Panzeca WayCincinnati, OH 45267-0056Mail Location: 0056Phone: 513-558-5704Email: ehgrad@uc.edu