Results 111 to 120 of about 3,113,449 (308)
Label-sensitive task grouping by Bayesian nonparametric approach for multi-task multi-label learning
, 2018 © 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus
V Nguyen (9860309) +5 more
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Molecular Oncology, EarlyView.We identify USP29 as the only DUB mirroring CA9 expression, a marker of hypoxia and HIF pathway activation associated with PCA aggressiveness. USP29 stabilizes HIF‐1α and HIF‐2α via a noncanonical mechanism that is independent of PHD/pVHL activity yet relies on proteasomal regulation, establishing USP29 as a previously unrecognized regulator of hypoxic
Amelie S Schober +16 more
wiley +1 more source
Frontiers in BioinformaticsIn the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA.
Shubham Choudhury +3 more
doaj +1 more source
Multi-Label Learning With Label Specific Features Using Correlation Information
IEEE Access, 2019 To deal with the problem where each instance is associated with multiple labels, a lot of multi-label learning algorithms have been developed in recent years.
Huirui Han +4 more
doaj +1 more source
Multi-instance multi-label learning
Artificial Intelligence, 2012 64 pages, 10 figures; Artificial Intelligence ...
Zhi-Hua Zhou +3 more
openaire +2 more sources
Graph Convolutional Multi-Label Hashing for Cross-Modal Retrieval
Cross-modal hashing encodes different modalities of multimodal data into low-dimensional Hamming space for fast cross-modal retrieval. In multi-label cross-modal retrieval, multimodal data are often annotated with multiple labels, and some labels, e.g.“,
Liu, Weiwei +5 more
core +1 more source
Multi-Label Classification with Label Graph Superimposing
, 2020 Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged
Wen, Shilei +6 more
core +1 more source
Finding novel vulnerabilities of hypomorphic BRCA1 alleles
Molecular Oncology, EarlyView.Synthetic lethality screens performed to identify novel vulnerabilities often model complete gene loss, thereby overlooking patient‐derived hypomorphic mutations. In this study, we have performed genome‐wide CRISPR screens on BRCA1 hypomorphic mutations, showing BRCA1I26A behaves like wild‐type, while BRCA1R1699Q mimics deficiency. Furthermore, we have
Anne Schreuder +10 more
wiley +1 more source
An Efficient Stacking Model of Multi-Label Classification Based on Pareto Optimum
IEEE Access, 2019 Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among
Wei Weng +4 more
doaj +1 more source
Multi-label zero-shot learning with graph convolutional networks
, 2020 Multi-label zero-shot learning with graph convolutional ...
C Domeniconi (13325145) +4 more
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