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SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

Advanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies. [PDF]

Aging (Albany NY)
Xiong Y, Li S, Bai Y, Chen T, Sun W, Chen L, Yu J, Sun L, Li C, Wang J, Wu B.   +10 more
europepmc   +1 more source

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Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Hongbin Zha
exaly   +3 more sources

Enhance explainability of manifold learning

Neurocomputing, 2022
Henry Han, Wentian Li
exaly   +2 more sources

Geometric Manifold Learning

IEEE Signal Processing Magazine, 2011
We present algorithms for analyzing massive and high-dimensional data sets motivated by theorems from geometry and topology. Optimization criteria for computing data projections are discussed and skew radial basis functions (sRBFs) for constructing nonlinear mappings with sharp transitions are demonstrated.
Jamshidi, Arta, Kirby, Michael, Broomhead, Dave   +2 more
openaire   +2 more sources

Active learning on manifolds

Neurocomputing, 2014
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose.
Cheng Li, Haifeng Liu 0001, Deng Cai 0001   +2 more
openaire   +1 more source

Multiscale Manifold Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Many high-dimensional data sets that lie on a low-dimensional manifold exhibit nontrivial regularities at multiple scales. Most work in manifold learning ignores this multiscale structure. In this paper, we propose approaches to explore the deep structure of manifolds.
Chang Wang 0001, Sridhar Mahadevan
openaire   +1 more source

Learning Invariance Manifolds

Neurocomputing, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Manifold Learning in Protein Interactomes

Journal of Computational Biology, 2011
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the ...
MARRAS, ELISABETTA, Travaglione A, Capobianco E.   +2 more
openaire   +3 more sources