Results 81 to 90 of about 364,885 (315)
Randomized Dimensionality Reduction for k-means Clustering [PDF]
, 2013 We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}.
Boutsidis, Christos +3 more
core
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
Morphological mapping for non‐linear dimensionality reduction
IET Computer Vision, 2015 Recently, much research has been carried out on dimensionality reduction techniques that summarise a large set of features into a smaller set, leading to much less redundancy.
Rajiv Kapoor, Rashmi Gupta
doaj +1 more source
Dimensionality Reduction in Gene Expression Data Sets
IEEE Access, 2019 Dimensionality reduction is used in microarray data analysis to enhance prediction quality, reduce computing time, and construct more robust models.
Jovani Taveira De Souza +2 more
doaj +1 more source
Subspace clustering of dimensionality-reduced data
, 2014 Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, assumed unknown.
Bölcskei, Helmut +2 more
core +1 more source
The anabolic steroid stanozolol is a potent inhibitor of human MutT homolog 1
FEBS Letters, EarlyView.MutT homolog 1 (MTH1) is a member of the NUDIX superfamily of enzymes and is an anticancer drug target. We show that stanozolol (Stz), an anabolic steroid, is an unexpected nanomolar inhibitor of MTH1. The X‐ray crystal structure of the human MTH1–Stz complex reveals a unique binding scaffold that could be utilized for future inhibitor development ...
Emma Scaletti Hutchinson +7 more
wiley +1 more source
Nonlinear Dimensionality Reduction Based on HSIC Maximization
IEEE Access, 2018 Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma +3 more
doaj +1 more source
Quantum Discriminant Analysis for Dimensionality Reduction and Classification
, 2016 We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set.
Cong, Iris, Duan, Luming
core +1 more source
Dimensional reduction for a Bayesian filter [PDF]
Proceedings of the National Academy of Sciences, 2004 An adaptive strategy is proposed for reducing the number of unknowns in the calculation of a proposal distribution in a sequential Monte Carlo implementation of a Bayesian filter for nonlinear dynamics. The idea is to solve only in directions in which the dynamics is expanding, found adaptively; this strategy is suggested by earlier work on optimal ...
Paul Krause, Alexandre J. Chorin
openaire +4 more sources
FEBS Letters, EarlyView.In this work, we reveal how different enzyme binding configurations influence the fluorescence decay of NAD(P)H in live cells using time‐resolved anisotropy imaging and fluorescence lifetime imaging microscopy (FLIM). Mathematical modelling shows that the redox states of the NAD and NADP pools govern these configurations, shaping their fluorescence ...
Thomas S. Blacker +8 more
wiley +1 more source