ICARE: An R package to build, validate and apply absolute risk models.
Ontology highlight
ABSTRACT:
SUBMITTER: Pal Choudhury P
PROVIDER: S-EPMC7001949 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:There has been increasing interest in assessing the impacts of extreme temperatures on mortality due to diseases of the circulatory system. This is further relevant for future climate scenarios where marked changes in climate are expected. This paper presents a solid method do identify the relationship between extreme temperatures and mortality risk by using as predictors simulated temperature data for cold and hot conditions in two urban areas in Portugal. METHODS:Based on the mortality and meteorological data from Porto Metropolitan Area (PMA) and Lisbon Metropolitan Area (LMA), a distributed lag nonlinear model (DLNM) was implemented to estimate the temperature effects on mortality due to diseases of the circulatory system. The performance of the models was validated via bootstrapping approaching by creating resamples with replacement from the validating data. Bootstrapping was also used to identify the best candidate model and to evaluate the sensitivity of the spline functions to the exposure-lag-response relationship. RESULTS:It is found that the model is able to reproduce the temperature-related mortality risk for two metropolitan areas. Temperature previously simulated by climate models is useful and even better than observed temperature. Although, the biases in predictions in both metropolitan areas are low, mortality risk predictions in PMA are more accurate than in LMA. Using parametric bootstrapping, we found that the overall cumulative association estimated under different bi-dimensional exposure-lag-response relationship are relatively stable, especially for the model selected by Quasi-Akaike Information Criteria (QAIC). Exposure to summer temperature conditions is best related to mortality risk. The association between winter temperature and mortality risk is somewhat less strong. CONCLUSIONS:The use of QAIC to choose from several candidate models provides valid predictions and reduced the uncertainty in the estimated relative risk for circulatory disease mortality. Our findings can be applied to better understand the characteristics and facilitate the prevention of circulatory disease mortality in Porto and Lisbon Metropolitan Areas, namely if we consider the actual context of climate change.
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Project description:Motivation:Mathematical models of cellular processes can systematically predict the phenotypes of novel combinations of multi-gene mutations. Searching for informative predictions and prioritizing them for experimental validation is challenging since the number of possible combinations grows exponentially in the number of mutations. Moreover, keeping track of the crosses needed to make new mutants and planning sequences of experiments is unmanageable when the experimenter is deluged by hundreds of potentially informative predictions to test. Results:We present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. We base our approach on a generic experimental workflow used in performing genetic crosses in budding yeast. We prove that the CrossPlan problem is NP-complete. We develop an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. We apply our method to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. We also extend our solution to incorporate other experimental conditions such as a delay factor that decides the availability of a mutant and genetic markers to confirm gene deletions. The experimental flow that underlies our work is quite generic and our ILP-based algorithm is easy to modify. Hence, our framework should be relevant in plant and animal systems as well. Availability and implementation:CrossPlan code is freely available under GNU General Public Licence v3.0 at https://github.com/Murali-group/crossplan. Supplementary information:Supplementary data are available at Bioinformatics online.
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Project description:BackgroundGenetic regulatory networks of sequence specific transcription factors underlie pattern formation in multicellular organisms. Deciphering and representing the mammalian networks is a central problem in development, neurobiology, and regenerative medicine. Transcriptional networks specify intermingled embryonic cell populations during pattern formation in the vertebrate neural tube. Each embryonic population gives rise to a distinct type of adult neuron. The homeodomain transcription factor Lbx1 is expressed in five such populations and loss of Lbx1 leads to distinct respecifications in each of the five populations.Methodology/principal findingsWe have purified normal and respecified pools of these five populations from embryos bearing one or two copies of the null Lbx1(GFP) allele, respectively. Microarrays were used to show that expression levels of 8% of all transcription factor genes were altered in the respecified pool. These transcription factor genes constitute 20-30% of the active nodes of the transcriptional network that governs neural tube patterning. Half of the 141 regulated nodes were located in the top 150 clusters of ultraconserved non-coding regions. Generally, Lbx1 repressed genes that have expression patterns outside of the Lbx1-expressing domain and activated genes that have expression patterns inside the Lbx1-expressing domain.Conclusions/significanceConstraining epistasis analysis of Lbx1 to only those cells that normally express Lbx1 allowed unprecedented sensitivity in identifying Lbx1 network interactions and allowed the interactions to be assigned to a specific set of cell populations. We call this method ANCEA, or active node constrained epistasis analysis, and think that it will be generally useful in discovering and assigning network interactions to specific populations. We discuss how ANCEA, coupled with population partitioning analysis, can greatly facilitate the systematic dissection of transcriptional networks that underlie mammalian patterning.
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