Results 61 to 70 of about 7,571,754 (304)

Learning Common and Label-Specific Features for Multi-Label Classification With Missing Labels

IEEE Access
Multi-label learning is a subfield of machine learning that addresses the issue of each instance belonging to numerous class labels at the same time. However, in some real applications, we can only receive a partial set of labels for each instance due to
Runxin Li, Zexian Ouyang, Zhenhong Shang, Lianyin Jia, Xiaowu Li   +4 more
doaj   +1 more source

LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification

IEEE Access, 2022
In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is
Farhad Gharebaghi, Ali Amiri
doaj   +1 more source

Structure‐forward targeting of claudins with synthetic binders

FEBS Letters, EarlyView.
Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley   +1 more source

Transductive Multi-View Zero-Shot Learning [PDF]

, 2015
(c) 2012. The copyright of this document resides with its authors.
Yanwei Fu, Tao Xiang, Fu, Y, Timothy M. Hospedales, Hospedales, T, Shaogang Gong, Xiang, T, Hospedales, TM, Gong, S   +8 more
core   +1 more source

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Deep Extreme Multi-label Learning [PDF]

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels. This paper is motivated to better explore the
Wenjie Zhang, Liwei Wang, Junchi Yan, Xiangfeng Wang 0001, Hongyuan Zha   +4 more
openaire   +3 more sources

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

Mathematics, 2022
Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain ...
Tianna Zhao, Yuanjian Zhang, Duoqian Miao   +2 more
doaj   +1 more source

Similarity-based Multi-label Learning [PDF]

2018 International Joint Conference on Neural Networks (IJCNN), 2018
Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size.
Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry, Rong Zhou 0001   +3 more
openaire   +2 more sources

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source