Results 221 to 230 of about 2,031,469 (352)
How to Generate a Good Word Embedding [PDF]
Siwei Lai, Kang Liu, Shizhu He, Jun Zhao
openalex +1 more source
Transcriptome Landscape of Cancer‐Associated Fibroblasts in Human PDAC
AP‐1 family members FOS and JUN regulate the malignant phenotype conversion of normal fibroblasts (NFs) to normal‐like cancer‐associated fibroblasts (nCAFs), while transforming growth factor‐β (TGFβ) and interferon‐γ (IFNγ) signals triggers the interconversion between classic myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs), respectively ...
Mengyu Tao+15 more
wiley +1 more source
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks. [PDF]
Bajaj G+7 more
europepmc +1 more source
This review explores the cutting‐edge development of bio‐integrated flexible electronics for real‐time hemodynamic monitoring in cardiovascular healthcare. It covers key physiological indicators, innovative sensing mechanisms, and materials considerations. This paper highlights the application of both invasive and non‐invasive devices in cardiovascular
Ke Huang, Zhiqiang Ma, Bee Luan Khoo
wiley +1 more source
Word, graph and manifold embedding from Markov processes
Tatsunori Hashimoto+2 more
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Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification [PDF]
Aditya Mogadala, Achim Rettinger
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Diallyl trisulfides (DATs) selectively induce cuproptosis in hepatic stellate cells (HSCs) by targeting Ras‐related protein Rab‐18 (RAB18) and regulating lipophagy. DATs promote RAB18 phase separation, enhance mitochondrial‐associated membrane structures (MAMs) formation, and increase succinylation of dihydrolipoamide dehydrogenase (DLD) at K320.
Haoyuan Tian+16 more
wiley +1 more source
Negative Associations in Word Embeddings Predict Anti-black Bias across Regions-but Only via Name Frequency. [PDF]
van Loon A+3 more
europepmc +1 more source