Results 61 to 70 of about 1,150,244 (261)
Fitting High-Dimensional Copulae to Data [PDF]
, 2011 This paper make an overview of the copula theory from a practical side. We consider different methods of copula estimation and different Goodness-of-Fit tests for model selection. In the GoF section we apply Kolmogorov-Smirnov and Cramer-von-Mises type tests and calculate power of these tests under different assumptions.
openaire +4 more sources
FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
wiley +1 more source
A network approach for low dimensional signatures from high throughput data
Scientific Reports, 2022 One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables—a signature—for sample classification purposes (diagnosis, prognosis, stratification).
Nico Curti +4 more
doaj +1 more source
Journal of the National Science Foundation of Sri Lanka, 2016 This paper provides a brief introduction to high-dimensional data, a form of ‘Big Data’, and gives an overview of several data analysis concepts and techniques that could be used to explore and analyse such data. An example that involves genomics data from several Sri Lankan and United Kingdom oral cancer patients is used to illustrate the methods.
Dhammika Amaratunga, Javier Cabrera
openaire +2 more sources
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
K-means clustering based filter feature selection on high dimensional data
IJAIN (International Journal of Advances in Intelligent Informatics), 2016 With hundreds or thousands of features in high dimensional data, computational workload is challenging. In classification process, features which do not contribute significantly to prediction of classes, add to the computational workload.
Dewi Pramudi Ismi +2 more
doaj +1 more source
FEBS Letters, EarlyView.Cancer progression is regulated by the dynamic matrix code of the tumor microenvironment, which influences cellular behavior and disease development. Importantly, matrix remodeling in three‐dimensional cancer models more accurately reflects in vivo conditions compared to conventional two‐dimensional systems.
Sylvia Mangani +3 more
wiley +1 more source
NN-Descent on High-Dimensional Data [PDF]
Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, 2018 K-nearest neighbor graphs (K-NNGs) are used in many data-mining and machine-learning algorithms. Naive construction of K-NNGs has a complexity of O(n2), which could be a problem for large-scale data sets. In order to achieve higher efficiency, many exact and approximate algorithms have been developed, including the NN-Descent algorithm of Dong ...
Brankica Bratic +4 more
openaire +1 more source
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
Dual Query: Practical Private Query Release for High Dimensional Data
The Journal of Privacy and Confidentiality, 2017 We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets. Like all algorithms for this task, ours necessarily has worst-case complexity exponential in the dimension of the data. However,
Marco Gaboardi +4 more
doaj +1 more source