Results 51 to 60 of about 351,687 (262)

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

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

Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells

Molecular Oncology, EarlyView.
Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son, Lily L. Remsing Rix, Bin Fang, Eric A. Welsh, Nicole V. Bremer, Valentina Foglizzo, Paola Roa, Nickole Sigcha‐Coello, Yi Liao, Eric B. Haura, Alexander Drilon, John M. Koomen, Emiliano Cocco, Uwe Rix   +13 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

Methylation biomarkers can distinguish pleural mesothelioma from healthy pleura and other pleural pathologies

Molecular Oncology, EarlyView.
We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck, Nele De Meulenaere, Thomas Vanpoucke, Joe Ibrahim, Dieter Peeters, Suresh Krishan Yogeswaran, Wen Wen, Paul Van Schil, Jeroen M. H. Hendriks, Jo Raskin, Jan van Meerbeeck, Guy Van Camp, Ken Op de Beeck   +12 more
wiley   +1 more source

Deep Multimodal Representation Learning: A Survey

IEEE Access, 2019
Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data.
Wenzhong Guo, Jianwen Wang, Shiping Wang
doaj   +1 more source

Perspectives in educating molecular pathologists on liquid biopsy: Toward integrative, equitable, and decentralized precision oncology

Molecular Oncology, EarlyView.
Liquid biopsy enables minimally invasive, real‐time molecular profiling through analysis of circulating biomarkers in biological fluids. This Perspective highlights the importance of training pathologists through integrative educational programs, such as the European Masters in Molecular Pathology, to ensure effective and equitable implementation of ...
Marius Ilié, Umberto Malapelle, Catherine Alix‐Panabières, Claus Lindbjerg Andersen, Sandra Chlebowski, Vivien Lake, Caroline Lacoux, Virginie Lespinet‐Fabre, Olivier Bordone, Simon Heeke, Christophe Bontoux, Ellen Heitzer, Klaus Pantel, Paul Hofman   +13 more
wiley   +1 more source

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 more
wiley   +1 more source

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

Molecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris, Vasiliki Siozopoulou, Léon C van Kempen, Anne Sieben, Ella Roelant, Stig Hellemans, Elyne Backx, Laure Sorber, Koen De Winne, Senada Koljenović, Karen Zwaenepoel   +10 more
wiley   +1 more source