Results 101 to 110 of about 4,317,504 (301)

Intrapatient tumour heterogeneity and clonal evolution in an autopsy study of metastatic salivary gland cancer

open access: yesMolecular Oncology, EarlyView.
Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche   +10 more
wiley   +1 more source

Exploiting label correlations for multi-label classification [PDF]

open access: yes, 2011
Multi-label classification is widely used for various applications such as automatic music tagging. Often, multi -label learning is done by transforming into multiple independent binary classification problems.
Li, Cheng-Xian
core   +1 more source

ADP‐ribosylation: An emerging regulator of the epigenome

open access: yesMolecular Oncology, EarlyView.
ADP‐ribosylation has emerged as a dynamic epigenetic signaling mechanism that modifies histones and chromatin‐associated proteins. Through coordinated PARylation and MARylation, it integrates with other histone modifications to regulate chromatin structure, transcription factor activity, and gene expression, influencing genome function and disease ...
Cristel V. Camacho   +2 more
wiley   +1 more source

Online Metric Learning for Multi-Label Classification [PDF]

open access: yes, 2019
Online multi-label classification has been widely used in various real world applications, such as twitter, facebook post, instagram, video search and RSS feeds. With the proliferation of multi-label classification, significant research efforts have been
Yang, Jiahui
core   +1 more source

A Multi-Label Text Categorization Algorithm Incorporating Label-Guided Attention Mechanisms

open access: yesIEEE Access
This paper proposes a multi-label text classification algorithm based on causal relationships to address the current challenge of accurately capturing label correlations in multi-label text classification tasks. The algorithm comprises a basic prediction
Shaocong Guo, Qian Hao
doaj   +1 more source

YIPFα1A expression is regulated by multilayered molecular mechanisms

open access: yesFEBS Open Bio, EarlyView.
YIPFα1A, a five‐pass Golgi protein, is regulated at multiple layers. (1) Rare‐codon enrichment drives translation‐coupled mRNA decay. (2) A proximal 3′‐UTR element stabilizes mRNA. (3) A distal 3′‐UTR element included by alternate poly(A) site usage represses translation, which can be overridden by the proximal 3′‐UTR element.
Tokio Takaji   +2 more
wiley   +1 more source

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning [PDF]

open access: yes, 2014
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies.
Plumbley, Mark D.   +6 more
core   +2 more sources

Multi-Label Image Classification by Feature Attention Network

open access: yesIEEE 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

UiO‐66 metal–organic frameworks in biomedicine: From structural tunability to bioimaging, photodiagnostics, and photodynamic cancer therapy

open access: yesFEBS Open Bio, EarlyView.
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová   +2 more
wiley   +1 more source

Multi-label approach for human-face classification

open access: yes, 2015
Single-label classification models have been widely used for human-face classification. In this paper, we present a multi-label classification approach for human-face classification.
Atul Sajjanhar (13059588)   +2 more
core  

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