Results 31 to 40 of about 101,283 (263)
Protein pyrophosphorylation by inositol pyrophosphates — detection, function, and regulation
FEBS Letters, EarlyView.Protein pyrophosphorylation is an unusual signaling mechanism that was discovered two decades ago. It can be driven by inositol pyrophosphate messengers and influences various cellular processes. Herein, we summarize the research progress and challenges of this field, covering pathways found to be regulated by this posttranslational modification as ...
Sarah Lampe +3 more
wiley +1 more source
Multi-Label Image Classification by Feature Attention Network
IEEE Access, 2019 Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model.
Zheng Yan +3 more
doaj +1 more source
Multi-Label Feature Selection Combining Three Types of Conditional Relevance
Entropy, 2021 With the rapid growth of the Internet, the curse of dimensionality caused by massive multi-label data has attracted extensive attention. Feature selection plays an indispensable role in dimensionality reduction processing.
Lingbo Gao +4 more
doaj +1 more source
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
Multi label AdaBoost Algorithm Based on Label Correlations
工程科学与技术, 2016 :In order to improve classification performance and exploit label correlations,AdaBoost.MLR algorithm was proposed.Cosine similarity was adopted to capture the complex correlations among labels in AdaBoost.MLR algorithm,a supplementary label matrix was ...
王莉莉, 付忠良
doaj
FEBS Letters, EarlyView.Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley +1 more source
A Hubness Information-Based k-Nearest Neighbor Approach for Multi-Label Learning
MathematicsMulti-label classification (MLC) plays a crucial role in various real-world scenarios. Prediction with nearest neighbors has achieved competitive performance in MLC. Hubness, a phenomenon in which a few points appear in the k-nearest neighbor (kNN) lists
Zeyu Teng +3 more
doaj +1 more source
MLGN:A Multi-Label Guided Network for Improving Text Classification
IEEE Access, 2023 Within natural language processing, multi-label classification is an important but challenging task. It is more complex than single-label classification since the document representations need to cover fine-grained label information, while the labels ...
Qiang Liu +6 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
Updating Correlation-Enhanced Feature Learning for Multi-Label Classification
MathematicsIn the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds
Zhengjuan Zhou +4 more
doaj +1 more source