Results 51 to 60 of about 1,150,244 (261)

Dynamic Feature Selection for Clustering High Dimensional Data Streams

IEEE Access, 2019
Change in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This
Conor Fahy, Shengxiang Yang
doaj   +1 more source

Structural insights into an engineered feruloyl esterase with improved MHET degrading properties

FEBS Letters, EarlyView.
A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa, Konstantinos Makryniotis, Theofani‐Iosifina Sousani, Evangelos Topakas, Vangelis Daskalakis, Maria Dimarogona   +5 more
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

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
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

Learning to visualise high-dimensional data [PDF]

Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004., 2004
Visualisation techniques focus on reducing high dimensional data to a low dimensional surface or a cube. Similar dimensional reduction is attempted in the so-called 'self-organising maps'. A number of techniques have been developed to visualise categories learnt by these maps through and exemplified by the term sequential clustering.
Ahmad, Khurshid, Vrusias, Bogdan
openaire   +2 more sources

Epigenetic blind spots – the role of DNA methylation dynamics in stem cell‐based models of embryogenesis

FEBS Letters, EarlyView.
Embryo‐like structures (stembryos) are an innovative tool, but they are hindered by experimental variability and limited developmental potential. DNA methylation is crucial for mammalian development, but its status in stembryo models is poorly characterized.
Sara Canil, Anna Corrà, Annalisa Scapolatiello, Chengxiang Qiu, Gianluca Amadei   +4 more
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