Results 41 to 50 of about 1,077,907 (260)

Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing

FEBS Letters, EarlyView.
Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
wiley   +1 more source

Evaluation of changes in prediction modelling in biomedicine using systematic reviews

BMC Medical Research Methodology
The number of prediction models proposed in the biomedical literature has been growing year on year. In the last few years there has been an increasing attention to the changes occurring in the prediction modeling landscape.
Lara Lusa, Franziska Kappenberg, Gary S. Collins, Matthias Schmid, Willi Sauerbrei, Jörg Rahnenführer, on behalf of the Topic Group for High-dimensional data of the STRATOS initiative   +6 more
doaj   +1 more source

Time after time – circadian clocks through the lens of oscillator theory

FEBS Letters, EarlyView.
Oscillator theory bridges physics and circadian biology. Damped oscillators require external drivers, while limit cycles emerge from delayed feedback and nonlinearities. Coupling enables tissue‐level coherence, and entrainment aligns internal clocks with environmental cues.
Marta del Olmo, Carolin Ector, Hanspeter Herzel   +2 more
wiley   +1 more source

Privacy-Preserving Data Sharing in High Dimensional Regression and Classification Settings

The Journal of Privacy and Confidentiality, 2012
We focus on the problem of multi-party data sharing in high dimensional data settings where the number of measured features (or the dimension) p is frequently much larger than the number of subjects (or the sample size) n, the so-called p >> n scenario ...
Stephen E. Fienberg, Jiashun Jin
doaj   +1 more source

Machine learning of high dimensional data on a noisy quantum processor

npj Quantum Information, 2021
Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space.
Evan Peters, João Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, Gabriel N. Perdue   +8 more
doaj   +1 more source

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

The newfound relationship between extrachromosomal DNAs and excised signal circles

FEBS Letters, EarlyView.
Extrachromosomal DNAs (ecDNAs) contribute to the progression of many human cancers. In addition, circular DNA by‐products of V(D)J recombination, excised signal circles (ESCs), have roles in cancer progression but have largely been overlooked. In this Review, we explore the roles of ecDNAs and ESCs in cancer development, and highlight why these ...
Dylan Casey, Zeqian Gao, Joan Boyes
wiley   +1 more source

Correlation based feature selection with clustering for high dimensional data

Journal of Electrical Systems and Information Technology, 2018
Feature selection is an essential technique to reduce the dimensionality problem in data mining task. Traditional feature selection algorithms are fail to scale on large space.
Smita Chormunge, Sudarson Jena
doaj   +1 more source

Stable ant‐antlion optimiser for feature selection on high‐dimensional data

Electronics Letters, 2021
High‐dimensional data exists widely in the real world, such as gene, magnetic resonance imaging (MRI), text, web data and so on. Feature selection is an effective and powerful method that is often adopted to reduce dimensions of high‐dimensional data for
Mengmeng Li, Wei Qin, Jinhui Zhang, Jichuan Wang, Qibin Zheng, Yi Liu   +5 more
doaj   +1 more source

OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV)

Barekeng, 2022
High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier.
Andi Harismahyanti A., Indahwati Indahwati, Anwar Fitrianto, Erfiani Erfiani   +3 more
doaj   +1 more source