Results 131 to 140 of about 5,846,406 (312)
Towards neural-symbolic integration: the evolutionary neural logic networks [PDF]
, 2004 This work presents the application of a new methodology for the production of neural logic networks into two real-world problems from the medical domain.
Tsakonas, Athanasios, A. Tsakonas
core +1 more source
Onasemnogene Abeparvovec in Patients With SMA: Interim Results of the RESTORE Registry in Japan
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective There are limited real‐world data regarding the safety and effectiveness of onasemnogene abeparvovec (OA; Zolgensma) infusion, a one‐time gene replacement therapy, for Japanese patients with spinal muscular atrophy (SMA). We aimed to improve understanding of the real‐world outcomes for OA in Japan.
Kayoko Saito +8 more
wiley +1 more source
Medical analysis and diagnosis by neural networks [PDF]
, 2010 In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background.
Brause, Rüdiger W.
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Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
Deep Learning as Applied in SAR Target Recognition and Terrain Classification
Leida xuebao, 2017 Deep learning such as deep neural networks has revolutionized the computer vision area. Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin. This paper reviews our works in the application
Xu Feng, Wang Haipeng, Jin Yaqiu
doaj +1 more source
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
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Focal cortical dysplasia (FCD) is the most common etiology of drug‐resistant epilepsy in children. Focal to bilateral tonic–clonic seizures (FBTCS) mark a high risk of drug‐resistant epilepsy and involve thalamocortical circuitry in their generation and propagation.
Hua Xie +8 more
wiley +1 more source
, 2017 Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors.
Junbin Gao +5 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To evaluate the expression of nine blood RNA biomarkers in a clinical trial based on genes previously identified in an experimental monkey model of stroke for diagnosis feasibility and prognostication. Methods IBIS‐CT1 was a prospective longitudinal study enrolling patients with ischemic stroke (IS) or intracerebral hemorrhage (ICH ...
Salomé Retailleau +11 more
wiley +1 more source
Deep subspace clustering networks [PDF]
, 2020 We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space.
Salzmann, Mathieu +4 more
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