Results 61 to 70 of about 2,965,444 (278)

The newfound relationship between extrachromosomal DNAs and excised signal circles

open access: yesFEBS Letters, EarlyView.
Extrachromosomal DNAs (ecDNAs) contribute to the progression of many human cancers. In addition, circular DNA by‐products of V(D)J recombination, excised signal circles (ESCs), have roles in cancer progression but have largely been overlooked. In this Review, we explore the roles of ecDNAs and ESCs in cancer development, and highlight why these ...
Dylan Casey, Zeqian Gao, Joan Boyes
wiley   +1 more source

Semi-Supervised Attribute Selection Algorithms for Partially Labeled Multiset-Valued Data

open access: yesMathematics
In machine learning, when the labeled portion of data needs to be processed, a semi-supervised learning algorithm is used. A dataset with missing attribute values or labels is referred to as an incomplete information system.
Yuanzi He   +3 more
doaj   +1 more source

An upstream open reading frame regulates expression of the mitochondrial protein Slm35 and mitophagy flux

open access: yesFEBS Letters, EarlyView.
This study reveals how the mitochondrial protein Slm35 is regulated in Saccharomyces cerevisiae. The authors identify stress‐responsive DNA elements and two upstream open reading frames (uORFs) in the 5′ untranslated region of SLM35. One uORF restricts translation, and its mutation increases Slm35 protein levels and mitophagy.
Hernán Romo‐Casanueva   +5 more
wiley   +1 more source

Semi-Supervised Learning for Neural Keyphrase Generation

open access: yes, 2018
We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large ...
Wang, Lu, Ye, Hai
core   +1 more source

In situ molecular organization and heterogeneity of the Legionella Dot/Icm T4SS

open access: yesFEBS Letters, EarlyView.
We present a nearly complete in situ model of the Legionella Dot/Icm type IV secretion system, revealing its central secretion channel and identifying new components. Using cryo‐electron tomography with AI‐based modeling, our work highlights the structure, variability, and mechanism of this complex nanomachine, advancing understanding of bacterial ...
Przemysław Dutka   +11 more
wiley   +1 more source

Contact Part Detection From 3D Human Motion Data Using Manually Labeled Contact Data and Deep Learning

open access: yesIEEE Access, 2023
Research on the interaction between users and their environment has been conducted in various fields, including human activity recognition (HAR), human-scene interaction (HSI), computer graphics (CG), and virtual reality (VR).
Changgu Kang   +3 more
doaj   +1 more source

Sequence determinants of RNA G‐quadruplex unfolding by Arg‐rich regions

open access: yesFEBS Letters, EarlyView.
We show that Arg‐rich peptides selectively unfold RNA G‐quadruplexes, but not RNA stem‐loops or DNA/RNA duplexes. This length‐dependent activity is inhibited by acidic residues and is conserved among SR and SR‐related proteins (SRSF1, SRSF3, SRSF9, U1‐70K, and U2AF1).
Naiduwadura Ivon Upekala De Silva   +10 more
wiley   +1 more source

An Instance Transfer-Based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

open access: yesIEEE Access, 2020
Recently, neural networks have shown promising results for named entity recognition(NER), which needs a number of labeled data to for model training. When meeting a new domain (target domain) for NER, there is no or a few labeled data, which makes domain
Chuanbo Liu   +3 more
doaj   +1 more source

Unsupervised Domain Adaptation by Backpropagation [PDF]

open access: yes, 2015
Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different ...
Ganin, Yaroslav, Lempitsky, Victor
core  

Refining Labeling Functions with Limited Labeled Data

open access: yesProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints. However, the quality of the generated labels depends directly on the accuracy of the LFs.
Chenjie Li   +4 more
openaire   +2 more sources

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