Results 71 to 80 of about 744,201 (267)
Structural components in functional data. [PDF]
, 2009 Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space.
Gasser, T +8 more
core +1 more source
Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Summed Component Analysis for Dimensionality Reduction and Classification
, 2013 In the area of dimensionality reduction, principal component analysis (PCA) has been used with much success. Other dimensionality reduction techniques have been proposed such as principal feature analysis (PFA) which was developed by Ira Cohen, Qi Tian ...
Ersoy, Okan, Sofolahan, Mopelola
core
Molecular Oncology, EarlyView.In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge +17 more
wiley +1 more source
Principal component analysis on space-time volume for action recognition
, 2018 Action recognition refers to the identification and classification of an action that is present in a given video. In our research, action recognition is performed by analysing data captured via RGB depth (RGB-D) cameras. The captured data representing an
Ng, Dan D., Wong, Ya Ping, Ng, Boon Yian
core
Federated Principal Component Analysis
, 2019 We present a federated, asynchronous, and $(\varepsilon, δ)$-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the ...
Grammenos, Andreas +3 more
openaire +3 more sources
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
Ensemble Principal Component Analysis
IEEE Access20 pages, 8 ...
Olga Dorabiala +2 more
openaire +3 more sources
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
Image encoding by independent principal components
, 2010 The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently,
Brause, Rüdiger W., Arlt, Björn
core