Results 71 to 80 of about 5,853,511 (292)
Learning on dynamic statistical manifolds
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020 Hyperbolic balance laws with uncertain (random) parameters and inputs are ubiquitous in science and engineering. Quantification of uncertainty in predictions derived from such laws, and reduction of predictive uncertainty via data assimilation, remain an open challenge.
F. Boso, D. M. Tartakovsky
openaire +5 more sources
Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
wiley +1 more source
S-Isomap++: Multi Manifold Learning from Streaming Data
, 2017 Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a challenge for such methods, owing to the high computational complexity ...
Chandola, Varun, Mahapatra, Suchismit
core +1 more source
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Manifold learning in statistical tasks
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 Many tasks of data analysis deal with high-dimensional data, and curse of dimensionality is an obstacle to the use of many methods for their solving.
A.V. Bernstein
doaj
Applied Sciences, 2021 Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical ...
Ümit Öztürk, Atınç Yılmaz
doaj +1 more source
Learning Invariant Riemannian Geometric Representations Using Deep Nets
, 2017 Non-Euclidean constraints are inherent in many kinds of data in computer vision and machine learning, typically as a result of specific invariance requirements that need to be respected during high-level inference.
Lohit, Suhas, Turaga, Pavan
core +1 more source
Homologous expression and purification of human HAX‐1 for structural studies
FEBS Open Bio, EarlyView.This research protocol provides detailed instructions for cloning, expressing, and purifying large quantities of the intrinsically disordered human HAX‐1 protein, N‐terminally fused to a cleavable superfolder GFP, from mammalian cells. HAX‐1 is predicted to undergo posttranslational modifications and to interact with membranes, various cellular ...
Mariana Grieben
wiley +1 more source
Manifold learning based on kernel density estimation
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks.
A.P. Kuleshov +2 more
doaj
Visualizing Energy Landscapes through Manifold Learning
Physical Review X, 2021 Energy landscapes provide a conceptual framework for structure prediction, and a detailed understanding of their topological features is necessary to develop efficient methods for their exploration.
Benjamin W. B. Shires +1 more
doaj +1 more source