An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links
Kimmel C, Visweswaran S. An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links. PLoS One, 2013 Nov 19; 8(11):e79564 doi: 10.137/journal.pone.0079564. PMID: 24260251. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0079564
Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods – which utilize a knowledge network derived from biological knowledge – have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.
We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.
The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.
Framework for Smart Electronic Health Record-Linked Predictive Models to Optimize Care for Complex Digestive Diseases
The objective of this research is to advance the use of decision analysis in biosurveillance. The specific aims of the research were to (1) construct decision analyses of representative biosurveillance decision problems using standard decision analytic techniques, and (2) deploy the underlying decision models in a decision-support system for analysts and epidemiologists.
Osmanbeyoglu HU, Lu KN, Oesterreich S, Day RS, Benos PV, Coronnello C, Lu X. Estrogen represses gene expression through reconfiguring chromatin structures. Nucleic Acids Research, 2013, 1–11. doi:10.1093/nar/gkt586.
Estrogen regulates over a thousand genes, with an equal number of them being induced or repressed. The distinct mechanisms underlying these dual transcriptional effects remain largely unknown. We derived comprehensive views of the transcription machineries assembled at estrogen-responsive genes through integrating multiple types of genomic data. In the absence of estrogen, the majority of genes formed higher-order chromatin structures, including DNA loops tethered to protein complexes involving RNA polymerase II (Pol II), estrogen receptor alpha (ERα) and ERα-pioneer factors. Genes to be ‘repressed’ by estrogen showed active transcription at promoters and throughout the gene bodies; genes to be ‘induced’ exhibited active transcription initiation at promoters, but with transcription paused in gene bodies. In the presence of estrogen, the majority of estrogen-induced genes retained the original higher-order chromatin structures, whereas most estrogen-repressed genes underwent a chromatin reconfiguration. For estrogen-induced genes, estrogen enhances transcription elongation, potentially through recruitment of co-activators or release of co-repressors with unique roles in elongation. For estrogen-repressed genes, estrogen treatment leads to chromatin structure reconfiguration, thereby disrupting the originally transcription-efficient chromatin structures. Our in silico studies have shown that estrogen regulates gene expression, at least in part, through modifying previously assembled higher-order complexes, rather than by facilitating de novo assembly of machineries.
Romagnoli KM, Handler SM, Hochheiser H. Home care: more than just a visiting nurse. BMJ Quality & Safety. doi:10.1136/bmjqs-2013-002339. Aug. 2013 PMID: 23940375 [PubMed in process]
When patients leave the hospital and return home with home nursing care, they go from highly supportive medical environments with potentially many physicians, nurses, aides and other professionals, to non-medical environments with formal and informal caregiver support frequently supplemented by visits from home care nurses. Patients and caregivers must struggle to absorb confusing and potentially contradictory information imparted both by multiple clinicians prior to discharge from the hospital and by home care nurses. Providers, for their part, often have incomplete understandings of home environments and patient and caregiver capabilities. Despite these difficulties, patients are largely left to themselves, expected to be engaged in their care sufficiently to own and manage their medical conditions. It is a daunting task.
Patient safety at home is as important as patient safety in hospitals. Unsafe conditions in the home can lead to unnecessary or avoidable hospitalisations.1 Home care decreases costs, improves health outcomes, and reduces hospital stays.2–8 Despite these benefits, problems exist. Around 13% of patients enrolled in home care experience an adverse event.9 ,10 The largest proportion of adverse events that occur among home care patients are related to medications, with 20–33% experiencing a medication problem or adverse drug event.11 ,12 Research has found that home care personnel and informal caregivers may play a role in a substantial subset of adverse events that result in hospitalisation,13 although further investigation is needed to understand the nature of the interaction. Insufficient attention to effective communication during transitional care from hospital to home may be one of the factors contributing to these patterns.1 ,14
Relatively little attention has been paid to the underlying causes of these adverse events and how they might be prevented. Our literature search revealed a limited number of published manuscripts in this domain compared to other settings. To prevent hospital readmissions, improve patient outcomes and save money, more attention must be paid to home care safety.
Doctoral Student, Intelligent Systems Program
BA (2002, Biology) University of Virginia
MS (2005, Physiology and Biophysics) Georgetown University
MD (2010, Medicine) Eastern Virginia Medical School
MS (2015, Biomedical Informatics) University of Pittsburgh
Jiang X, Barmada MM, Becich MJ. Evaluating de novo locus-disease discoveries in GWAS using the signal-to-noise ratio. AMIA Annu Symp Proc. 2011;2011:617-24. Epub 2011 Oct 22. PMID: 22195117
Gullapalli RR, Lyons-Weiler M, Petrosko P, Dhir R, Becich MJ, LaFramboise WA. Clinical integration of next-generation sequencing technology. Clin Lab Med. 2012 Dec;32(4):585-99. doi: 10.1016/j.cll.2012.07.005. Review. PMID: 23078661