PubMed · 7784546
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
S B Dowd, R Bower. Computer-based instruction.. https://pubmed.ncbi.nlm.nih.gov/7784546/
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.
Partially folded states of proteins are found to occur with a wide variety of degrees of unfolding, ranging from the compact molten globule to the fully unfolded forms, depending on solvent conditions and the specific protein involved. Small to intermediate angle X-ray scattering from partially folded states of proteins yields low resolution scattering profiles that may be used to explore the degree of folding of a protein under given solution conditions. By Monte Carlo simulation of a highly simplified homopolymer model, we show that such partially folded states will yield a characteristic scattering profile that may be written as a linear superposition of scattering from a compact core and of scattering from random coil loops that emerge from this core. We also find a term resulting from interference of X-rays scattering from the core with those scattering from the loops. This interference term oscillates in sign and tends to enhance the core portion of the scattering profile. We compare the model calculations of the scattering profile with measurements of the scattering profile as a function of salt concentration for cytochrome c at pH 2. Because of our characterization of the scattering profiles, we suggest that these results may be re-interpreted in terms of the presence of a range of partially folded states as a function of pH and salt concentration, and that the observed scattering profiles are consistent with the characterization of the partially folded states in terms of random coil loops emerging from a compact core with the loop fraction increasing as the salt concentration is decreased. This characterization is consistent with data on amide protection against H-2H exchange of compact regions within partially folded states observed for a number of proteins, including cytochrome c.