Results 91 to 100 of about 4,092,880 (310)
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.Dimethyl fumarate (DMF) reduces growth of HPV‐positive cervical cancer spheroids and induces ferroptosis in cervical cancer cells via blocking SLC7A11/Glutathione (GSH) axis. Combination of subcytotoxic doses of DMF and cisplatin (CDDP) further suppresses spheroid growth and drives cell death in 2D culture models.
Carolina Punziano +6 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
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
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
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
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
Detecting anomalies from high-dimensional wireless network data streams: a case study
, 2011 In this paper, we study the problem of anomaly detection in wireless network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from multi-dimensional or high ...
Zhang, Ji +3 more
core +1 more source
A blocking and regularization approach to high dimensional realized covariance estimation [PDF]
, 2009 We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix ...
Hautsch, Nikolaus +6 more
core +1 more source