Results 71 to 80 of about 1,253,621 (308)
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 +8 more
doaj +1 more source
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source
The merit of high-frequency data in portfolio allocation [PDF]
, 2011 This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator.
Hautsch, Nikolaus +5 more
core +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 +5 more
doaj +1 more source
Molecular Oncology, EarlyView.Targeted therapy was evaluated in SHH medulloblastoma using neuroepithelial stem cell (NES) and tumor‐derived NES‐like (tNES) models in 2D monolayers and 3D spheroids. PI3K, AKT, and CDK4/6 inhibitors had minimal effects in NES but markedly reduced viability and growth and induced apoptosis in tNES cells, revealing distinct therapeutic vulnerabilities.
Monika Lukoseviciute +4 more
wiley +1 more source
A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects [PDF]
In this paper we describe an alternative iterative approach for the estimation of linear regression models with high-dimensional fixed-effects such as large employer-employee data sets.
Guimaraes, Paulo, Portugal, Pedro
core +2 more sources
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
Molecular Oncology, EarlyView.Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova +14 more
wiley +1 more source