Results 111 to 120 of about 2,238,851 (367)

Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning

open access: yesInternational 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
doaj   +1 more source

ShcD adaptor protein drives invasion of triple negative breast cancer cells by aberrant activation of EGFR signaling

open access: yesMolecular 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

Targeting the AKT/mTOR pathway attenuates the metastatic potential of colorectal carcinoma circulating tumor cells in a murine xenotransplantation model

open access: yesMolecular 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

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

open access: yesBMC 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
doaj   +1 more source

Lazy Evaluation of Convolutional Filters [PDF]

open access: yes, 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]

open access: yes, 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

open access: yesMolecular 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

open access: yesAdvances 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
doaj   +1 more source

EMT‐associated bias in the Parsortix® system observed with pancreatic cancer cell lines

open access: yesMolecular 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

open access: yesMATEC 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

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