Results 131 to 140 of about 25,867,085 (318)

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs. [PDF]

Ophthalmol Sci, 2023
Lemij HG, Vente C, Sánchez CI, Vermeer KA.   +3 more
europepmc   +1 more source

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data [PDF]

Journal of Computational Physics, 2019
Yinhao Zhu, N. Zabaras, P. Koutsourelakis, P. Perdikaris   +3 more
semanticscholar   +1 more source

Data from Preclinical Evaluation of ²²⁵Ac-Labeled Single-Domain Antibody for the Treatment of HER2^pos Cancer

, 2023
Magdalena Rodak, Yana Dekempeneer, Maria Wojewódzka, Vicky Caveliers, Peter Covens, Brian W. Miller, Matthijs B. Sevenois, Frank Bruchertseifer, Alfred Morgenstern, Tony Lahoutte, Matthias D’Huyvetter, Marek Pruszyński   +11 more
openalex   +1 more source

PYCR1 inhibition in bone marrow stromal cells enhances bortezomib sensitivity in multiple myeloma cells by altering their metabolism

Molecular Oncology, EarlyView.
This study investigated how PYCR1 inhibition in bone marrow stromal cells (BMSCs) indirectly affects multiple myeloma (MM) cell metabolism and viability. Culturing MM cells in conditioned medium from PYCR1‐silenced BMSCs impaired oxidative phosphorylation and increased sensitivity to bortezomib.
Inge Oudaert, Lauren van den Broecke, Osman Aksoy, Judith Lind, Sonia Vallet, Arne Van der Vreken, Gamze Ates, Ann Massie, Ken Maes, Kim De Veirman, Elke De Bruyne, Karin Vanderkerken, Klaus Podar, Eline Menu   +13 more
wiley   +1 more source

Comparative Analysis of Self-Labeled Algorithms for Predicting MOOC Dropout: A Case Study

Applied Sciences
Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learner ...
George Raftopoulos, Georgios Kostopoulos, Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas   +5 more
doaj   +1 more source

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data. [PDF]

Entropy (Basel), 2022
Cubero L, Castelli J, Simon A, de Crevoisier R, Acosta O, Pascau J.   +5 more
europepmc   +1 more source

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

, 2020
Gizem Karagoz, Adnan Yazıcı, Tansel Dökeroğlu, Ahmet Coşar   +3 more
openalex   +2 more sources

Data from The Oral PI3Kδ Inhibitor Linperlisib for the Treatment of Relapsed and/or Refractory Follicular Lymphoma: A Phase II, Single-Arm, Open-Label Clinical Trial

, 2023
Tingyu Wang, Xiuhua Sun, Lihua Qiu, Hang Su, Junning Cao, Zhiming Li, Yuqin Song, Li Zhang, Dengju Li, Huijing Wu, Wei Zhang, Junmin Li, Keshu Zhou, Hui Zhou, Yu Yang, Zhifeng Li, Hong Cen, Zhen Cai, Zhihui Zhang, Weijun Fu, Jie Jin, Fei Li, Weixin Wu, Xuekui Gu, Weiliang Zhu, Lihong Liu, Zengjun Li, Shuhua Yi, Hanying Bao, Zusheng Xu, Lugui Qiu   +30 more
openalex   +1 more source

In vitro properties of patient serum predict clinical outcome after high dose rate brachytherapy of hepatocellular carcinoma

Molecular Oncology, EarlyView.
Following high dose rate brachytherapy (HDR‐BT) for hepatocellular carcinoma (HCC), patients were classified as responders and nonresponders. Post‐therapy serum induced increased BrdU incorporation and Cyclin E expression of Huh7 and HepG2 cells in nonresponders, but decreased levels in responders.
Lukas Salvermoser, Jan Niklas Schäfer, Shraga Nahum Goldberg, Philipp Maximilian Kazmierczak, Moritz Nikolaus Gröper, Philipp Franz Linden, Elif Öcal, Tanja Burkard, Stefanie Corradini, Najib Ben Khaled, Moritz Wildgruber, Max Seidensticker, Jens Ricke, Matthias Stechele, Marianna Alunni‐Fabbroni   +14 more
wiley   +1 more source

A Self-Supervised Contrastive Framework for Specific Emitter Identification with Limited Labeled Data

Remote Sensing
Specific Emitter Identification (SEI) is a specialized technique for identifying different emitters by analyzing the unique characteristics embedded in received signals, known as Radio Frequency Fingerprints (RFFs), and SEI plays a crucial role in ...
Jiaqi Wang, Lishu Guo, Pengfei Liu, Peng Shang, Xiaochun Lu, Hang Zhao   +5 more
doaj   +1 more source