PubMed · 14647491
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Harald Brune. 2003. Surface diffusion: shifting strings.. https://doi.org/10.1038/nmat1027
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.
The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.
The objective of the work described in this paper is to develop a means for characterizing the validity of an empirical methodology for detecting signals potentially related to complicated adverse event (AE) coding terms in multidimensional public health surveillance data. The signal detection tool under evaluation is the multi-item gamma Poisson shrinkage (MGPS) estimation program. We were interested in its potential application to passive surveillance system monitoring, to screen for 'signals' of complicated adverse event coding terms (AE terms) in complex and noisy data. The research was to design and produce a flexible and user-friendly utility for probabilistically defining complicated signals in a database, iterating large numbers of applications of the MGPS detection algorithm and establishing proportions of correct detection events. We sought to establish the specificity of the MGPS by developing a random background using a gradient that ranged from rigorous (but not very relevant) to relevant (but noisy). To establish the sensitivity, signals were defined based on recognized public health issues of interest (such as the introduction of a new vaccine into the population). Methods of representing a signal included a simple pair-wise association consisting of a new vaccine and one AE term, as well as a more realistic complex of multiple AE terms comprising a 'syndrome'. A web application has been developed to create and insert signals with user-defined probabilities in multiple iterations of simulated random background data. Three forms of simulated data based on the vaccine adverse event reporting system (VAERS) cumulative spontaneous database were defined to serve as background noise against which to contrast introduced vaccine adverse event signals: (1) completely random associations between vaccines and AE terms, (2) random associations of vaccine sets and AE term sets preserving naturally observed vaccine co-occurrences and AE term co-occurrences and (3) samples from the actual VAERS data as reported. Rates of detection by the MGPS algorithm can be established for specific signal patterns at varying probabilistic intensities in a choice of random background data forms. Knowing these rates is important for determining the degree of response to an MGPS signal detection event in 'live' data.