Results 61 to 70 of about 2,204,167 (303)
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 +3 more
doaj +3 more sources
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 +3 more
doaj +1 more source
Liquid biopsy epigenetics: establishing a molecular profile based on cell‐free DNA
Molecular Oncology, EarlyView.Cell‐free DNA (cfDNA) fragments in plasma from cancer patients carry epigenetic signatures reflecting their cells of origin. These epigenetic features include DNA methylation, nucleosome modifications, and variations in fragmentation. This review describes the biological properties of each feature and explores optimal strategies for harnessing cfDNA ...
Christoffer Trier Maansson +2 more
wiley +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 +4 more
doaj +1 more source
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 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
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +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 +13 more
wiley +1 more source
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning [PDF]
, 2021 Pan Zhou +3 more
openalex +3 more sources
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 +12 more
wiley +1 more source