Results 71 to 80 of about 273,362 (259)
FEBS Letters, EarlyView.Embryo‐like structures (stembryos) are an innovative tool, but they are hindered by experimental variability and limited developmental potential. DNA methylation is crucial for mammalian development, but its status in stembryo models is poorly characterized.
Sara Canil +4 more
wiley +1 more source
Exploring Correlation between Labels to improve Multi-Label Classification
CoRR, 2015 This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models were constructed, with SVM giving the best results ...
Amit Garg +4 more
openaire +2 more sources
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
Fuzzy Multi-label AdaBoost Algorithm for Heart Valve Disease Classification
工程科学与技术, 2017 :An improved fuzzy algorithm based on multi-label AdaBoost was proposed for classifing heart valve diseases.To reflect the severity of the disease,a membership function was used to map ultrasonic diagnosis to a fuzzy value in ［0, 1］,and the ultrasonic ...
王莉莉, 付忠良
doaj
Journal of Data and Information ScienceMany science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed
Xu Shuo, Zhang Yuefu, An Xin, Pi Sainan
doaj +1 more source
Federated Learning With Only Positive Labels by Exploring Label Correlations
IEEE Transactions on Neural Networks and Learning SystemsFederated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data w.r.t.
Xuming An +7 more
openaire +3 more sources
FEBS Letters, EarlyView.This study reveals that the small GTPase Rab14 is necessary for human papillomavirus (HPV) infection and plays an essential role in the transport of virions to the trans‐Golgi network (TGN). HPV in the early endosome (EE), which harbors GTP‐bound Rab14, is transported to the TGN through the switch of Rab14 from its GTP‐bound to GDP‐bound form.
Yoshiyuki Ishii, Iwao Kukimoto
wiley +1 more source
Soft-label recover based label-specific features learning
Scientific ReportsPresently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem.
Jiansheng Jiang +4 more
doaj +1 more source
Relative Entropy and PageRank-Based Classifier Chains for Multi-Label Classification
IEEE AccessThe Classifier Chains (CC) method is an effective method for multi-label classification, with its performance significantly contingent on the label order.
Xinyu Li, Jiaman Ding, Shuang Hu
doaj +1 more source
Learning label-label correlations in Extreme Multi-label Classification via Label Features
CoRRExtreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label
Siddhant Kharbanda +5 more
openaire +2 more sources