Results 71 to 80 of about 139,695 (308)
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
FEBS Letters, EarlyView.Septin 9 polybasic domains couple phosphoinositide‐rich membrane binding to centrosome positioning, Golgi organization, and microtubule acetylation to control epithelial polarity. Their loss disrupts this axis, causing centrosome mispositioning, Golgi fragmentation, reduced microtubule acetylation, and polarity inversion via upregulation of the ...
Ting ting Cai +4 more
wiley +1 more source
BIP: A dimensionality reduction for image indexing
ICT Express, 2019 Searching on internet is one of the daily task done by millions of users around the Globe. There is an urge for effective indexing scheme for unstructured data, which provide better search results. The image, content report, and site pages are said to be
Minu R.I. +3 more
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Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Non‐linear dimensionality reduction using fuzzy lattices
IET Computer Vision, 2013 The proposed method is based on extraction of non‐linearity from the nearest neighbourhood elements of image. To detect non‐linearity, relation between the nearest neighbourhood elements of the image, have been expressed in terms of Gaussian membership ...
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
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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
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
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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
Dimensionality reduction method for hyperspectral image analysis based on rough set theory
European Journal of Remote Sensing, 2020 High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction.
Zhenhua Wang +5 more
doaj +1 more source