Results 111 to 120 of about 2,238,851 (367)
Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning
International Journal of Computational Intelligence Systems, 2020 One of the current trends in the deep neural network technology consists in allowing a man–machine interaction and providing an explanation of network design and learning principles. In this direction, an experience with fuzzy systems is of great support.
Vojtech Molek, Irina Perfilieva
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Molecular Oncology, EarlyView.We identified adaptor protein ShcD as upregulated in triple‐negative breast cancer and found its expression to be correlated with reduced patient survival and increased invasion in cell models. Using a proteomic screen, we identified novel ShcD binding partners involved in EGFR signaling pathways.
Hayley R. Lau +11 more
wiley +1 more source
Molecular Oncology, EarlyView.Dual targeting of AKT and mTOR using MK2206 and RAD001 reduces tumor burden in an intracardiac colon cancer circulating tumor cell xenotransplantation model. Analysis of AKT isoform‐specific knockdowns in CTC‐MCC‐41 reveals differentially regulated proteins and phospho‐proteins by liquid chromatography coupled mass spectrometry. Circulating tumor cells
Daniel J. Smit +19 more
wiley +1 more source
BMC Anesthesiology, 2021 Background Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain
Jian Zhan +6 more
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Lazy Evaluation of Convolutional Filters [PDF]
, 2016 In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network. This allows to trade-off the accuracy of a deep neural network with the computational and memory requirements.
Bohez, Steven +7 more
core +2 more sources
Deep Neural Network Ensembles [PDF]
, 2019 Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality.
openaire +3 more sources
Ubiquitination of transcription factors in cancer: unveiling therapeutic potential
Molecular Oncology, EarlyView.In cancer, dysregulated ubiquitination of transcription factors contributes to the uncontrolled growth and survival characteristics of tumors. Tumor suppressors are degraded by aberrant ubiquitination, or oncogenic transcription factors gain stability through ubiquitination, thereby promoting tumorigenesis.
Dongha Kim, Hye Jin Nam, Sung Hee Baek
wiley +1 more source
Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms
Advances in Distributed Computing and Artificial Intelligence Journal, 2020 Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation.
Noor Fatima
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EMT‐associated bias in the Parsortix® system observed with pancreatic cancer cell lines
Molecular Oncology, EarlyView.The Parsortix® system was tested for CTC enrichment using pancreatic cancer cell lines with different EMT phenotypes. Spike‐in experiments showed lower recovery of mesenchymal‐like cells. This was confirmed with an EMT‐inducible breast cancer cell line.
Nele Vandenbussche +8 more
wiley +1 more source
Application of deep learning for division of petroleum reservoirs
MATEC Web of Conferences, 2018 Traditional methods of dividing petroleum reservoirs are inefficient, and the accuracy of onehidden-layer BP neural network is not ideal when applied to dividing reservoirs.
Qin Yaqiong, Ye Zhaohui, Zhang Conghui
doaj +1 more source