Dr. Gregory Alexander recently awarded AHRQ grant.
October 9, 2017
Dr. Gregory Alexander, from The Sinclair School of Nursing, was recently awarded a $1,995,522.00 AHRQ grant. This grant will support an interdisciplinary research team who are already contributing to clinical research in long-term care settings. The PI is a doctorally-prepared RN and fellow in the American Academy of Nursing with over two decades of work […]
Dr. Elizabeth King recently awarded National Science Foundation grant
September 13, 2017
Drs. Troy Zars and Elizabeth King, in the Division of Biological Sciences, were recently awarded a $462,900 National Science Foundation grant! This grant will be providing funding for a project which will focus on how genes underlie variation in learning and memory performance in fruit flies. Additionally, this grant will support an outreach program called […]
J. Chris Pires Celebrated for Research Contributions
July 27, 2017
J. Chris Pires , a Professor in the Division of Biological Sciences, was selected for the 2017 Chancellor’s Award for Outstanding Research and Creative Activity. The award is given once a year to a professor who has made outstanding contributions in research and has great promise for achieving wider recognition. It is one of the highest […]
2206A Student Center
A Pilot Study of the Relationships between Elder-fall Alert and Medication History
Falls can happen anytime, anywhere, and at any age that result in injury and economic costs. When a patient falls in a hospital or a nursing home healthcare will be negatively affected. The systematic fall prevention program is necessary for any healthcare organization, and should be made as a daily work routine. The fall prevention program can reduce and limit effectively the number of patient falls as illustrated in the literature.
TigerPlace in Columbia, MO is a nursing home with an aging-in-place model. The analysis sensors (motion sensor, bed sensor, and gait sensor) have been installed and monitored continuously in the homes of seniors for the purpose of detecting early signs of health change. All data have been captured and stored. The alert of fall prediction will be distributed automatically to healthcare staff by visualization interface and E-mail in the system when the confidence reaches the threshold. The alert facilitates early interventions.
The continuous fall prediction alert indicates when the elder has physiologically changed, and is under the risk of falls. Medication, diagnostic testing, surgery, and other disease all can be the reason and cause the fall to happen. In this study, I will focus on the elder’s medication history, and try to create a model that illustrates the relationships between alerts and medication.
240 Naka Hall
Contrast mining to discover combinations of genetic factors associated with autism subgroups
Autism is characterized by a complex set of behavioral, social, and cognitive deficits. Extensive variation of these phenotypes suggests the existence of autism subtypes that likely have distinct genetic etiologies. The lack of unifying genotypes common to autism patients supports this subtype structure, and suggests that the onset of autism is due to combinations of genetic factors. The ability to precisely diagnose autism subtypes using genetic markers would lead to earlier and more specific treatments and improve outcomes, stressing the need for research which increases our understanding of the genetic etiologies of autism subtypes. In this research, we identify combinations of genetic factors that are associated with groups of autism patients with unifying behavioral profiles, yielding candidate genes to be investigated for their role in the development of these potential autism subtypes. Utilizing methods that combine bioinformatics strategies with data mining practices, we pursue three goals: the discovery of genetic combinations associated to a disease subgroup, the exploration of disease subgroups to find potential subtypes, and the analysis of relationships between genes and subgroups to identify relevant functional interactions.
Winston Haynes, PhD CandidateDate:
2206 A&B Student Center
Understanding disease through integrated molecular and clinical analyses
Abstract:Traditional biomedical experiments are designed to study a single cohort for a single disease using a single technology. By studying disease with such a narrow lens, researchers make discoveries that are not reproducible because they are not representative of the real heterogeneity of disease. By integrating data from over 40 studies and 7,000 patients, we establish a robust signature of disease which correlates with disease activity and persists across blood, tissue, and sorted cell populations. We compare relationships of 104 diseases based on molecular and clinical manifestations from 41,000 gene expression samples and 2 million patient records. Finally, we contextualize single-cell RNA-seq data with bulk gene expression profiles to understand the relationships of novel cell subsets to known cell populations and human disease. By integrating biomedical datasets, my work has enabled an unbiased and multi-scale understanding of disease.
Bio: Winston Haynes is a PhD candidate in biomedical informatics at Stanford University. His research focuses on developing methods to improve understanding of disease through unbiased analyses of heterogeneous, publicly available data. Building off his discovery that publications are biased towards well-annotated genes instead of those with the strongest disease associations, his work integrates molecular and clinical evidence to identify overlooked aspects of disease, including therapeutically actionable relationships between seemingly disparate diseases and novel molecular pathways associated with disease activity