Results 91 to 100 of about 424,943 (312)
Re-Ranking and Representations for Time Series Retrieval: A Comparative Study
IEEE AccessProperly understanding the trends and patterns of multiple variables over time is important for decision-making in several applications, ranging from ecological monitoring based on vegetation index time series to health condition assessment based on ...
Bionda Rozin +2 more
doaj +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
Time Series Clustering Method Based on Contrastive Learning [PDF]
Jisuanji kexueIt is difficult to intuitively define the similarity between time series by deep clustering methods which rely heavily on complex feature extraction networks and clustering algorithms.Contrastive learning can define the interval similarity of time series
YANG Bo, LUO Jiachen, SONG Yantao, WU Hongtao, PENG Furong
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Similarity Preserving Representation Learning for Time Series Clustering [PDF]
, 2019 Qi Lei +4 more
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Compact representation of temporal processes in echosounder time series via matrix decomposition [PDF]
, 2020 Wu-Jung Lee, Valentina Staneva
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A new network representation for time series analysis from the perspective of combinatorial property of ordinal patterns [PDF]
, 2023 Yun Lu +6 more
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Multi-task self-supervised time-series representation learning
Information SciencesTime-series representation learning can extract representations from data with temporal dynamics and sparse labels. When labeled data are sparse but unlabeled data are abundant, contrastive learning, i.e., a framework to learn a latent space where similar samples are close to each other while dissimilar ones are far from each other, has shown ...
Heejeong Choi, Pilsung Kang
openaire +2 more sources
Molecular Oncology, EarlyView.To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley +1 more source
Financial time series classification method based on low‐frequency approximate representation
Engineering ReportsAiming at the mode mixing problems of high frequency information caused by fluctuation agglomeration and pointed peak thick tail of financial time series, a time series classification method based on low frequency approximate representation is proposed ...
Bing Liu, Huanhuan Cheng
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Molecular Oncology, EarlyView.This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns +10 more
wiley +1 more source