Results 61 to 70 of about 2,210,762 (272)

Research and development of network representation learning

网络与信息安全学报, 2019
Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space.These vectors can be used as input to the machine learning model for social ...
Ying YIN, Lixin JI, Ruiyang HUANG, Lixin DU   +3 more
doaj   +3 more sources

An overview on data representation learning: From traditional feature learning to recent deep learning

Journal of Finance and Data Science, 2016
Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either “shallow” or “deep”.
Guoqiang Zhong, Li-Na Wang, Xiao Ling, Junyu Dong   +3 more
doaj   +1 more source

Entity Profiling in Knowledge Graphs

IEEE Access, 2020
Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs.
Xiang Zhang, Qingqing Yang, Jinru Ding, Ziyue Wang   +3 more
doaj   +1 more source

Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification

Diagnostics, 2022
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data.
Aiham Taleb, Csaba Rohrer, Benjamin Bergner, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Christoph Lippert, Joachim Krois   +7 more
doaj   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Global representation fine-tuning for federated self-supervised representation learning

International Journal of Intelligent Networks
Federated self-supervised representation learning combines federated learning with self-supervised mechanisms to learn general representations from distributed unlabeled data, effectively reducing reliance on labeled data.
Hongzi Li, Guifen Zhang, Qinchun Su, Lina Ge   +3 more
doaj   +1 more source

Network Representation Based on the Joint Learning of Three Feature Views

Big Data Mining and Analytics, 2019
Network representation learning plays an important role in the field of network data mining. By embedding network structures and other features into the representation vector space of low dimensions, network representation learning algorithms can provide
Zhonglin Ye, Haixing Zhao, Ke Zhang, Zhaoyang Wang, Yu Zhu   +4 more
doaj   +1 more source

Context-Aware Deep Markov Random Fields for Fake News Detection

IEEE Access, 2021
Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news ...
Tien Huu Do, Marc Berneman, Jasabanta Patro, Giannis Bekoulis, Nikos Deligiannis   +4 more
doaj   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source