Evolving Neural Arrays A new mechanism for learning complex action sequences
CLEI Electronic Journal, 2018 Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially ...
Leonardo Corbalán, Laura Lanzarini
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PIPENN: protein interface prediction from sequence with an ensemble of neural nets. [PDF]
Bioinformatics, 2022 Stringer B +5 more
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FEBS Open Bio, EarlyView.A tri‐culture of iPSC‐derived neurons, astrocytes, and microglia treated with ferroptosis inducers as an Induced ferroptosis model was characterized by scRNA‐seq, cell survival, and cytokine release assays. This analysis revealed diverse microglial transcriptomic changes, indicating that the system captures key aspects of the complex cellular ...
Hongmei Lisa Li +6 more
wiley +1 more source
A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN. [PDF]
Wirel Pers Commun, 2023 Naskath J, Sivakamasundari G, Begum AAS.
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BMI‐1 modulation and trafficking during M phase in diffuse intrinsic pontine glioma
FEBS Open Bio, EarlyView.The schematic illustrates BMI‐1 phosphorylation during M phase, which triggers its translocation from the nucleus to the cytoplasm. In cycling cells, BMI‐1 functions within the PRC1 complex to mediate H2A K119 monoubiquitination. Following PTC596‐induced M phase arrest, phosphorylated BMI‐1 dissociates from PRC1 and is exported to the cytoplasm via its
Banlanjo Umaru +6 more
wiley +1 more source
Meta‐analysis fails to show any correlation between protein abundance and ubiquitination changes
FEBS Open Bio, EarlyView.We analyzed over 50 published proteomics datasets to explore the relationship between protein levels and ubiquitination changes across multiple experimental conditions and biological systems. Although ubiquitination is often associated with protein degradation, our analysis shows that changes in ubiquitination do not globally correlate with changes in ...
Nerea Osinalde +3 more
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MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets. [PDF]
Front Cell Dev Biol, 2021 Koh W, Hoon S.
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FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN) [PDF]
Journal of Engineering Science and Technology, 2010 In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS,
LAHEEB MOHAMMAD IBRAHIM
doaj
Real-time support for high performance aircraft operation [PDF]
The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated.
Vidal, Jacques J.
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