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[PDF] Top 20 Deep learning for computational phenotyping in cell-based assays

Has 10000 "Deep learning for computational phenotyping in cell-based assays" found on our website. Below are the top 20 most common "Deep learning for computational phenotyping in cell-based assays".

Deep learning for computational phenotyping in cell-based assays

Deep learning for computational phenotyping in cell-based assays

... Summary: Computational phenotyping is an emergent set of technologies for systematically studying the role of the genome in eliciting phenotypes, the observable characteristics of an organism ... Voir le document complet

207

Reinforcement learning-based cell selection in sparse mobile crowdsensing

Reinforcement learning-based cell selection in sparse mobile crowdsensing

... (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality ...different cell sets will probably lead to diverse levels of inference data quality, cell ... Voir le document complet

14

Beyond predictive modeling : new computational aspects for deep learning based biological applications

Beyond predictive modeling : new computational aspects for deep learning based biological applications

... challenge for the design of peptide vaccines is the diversity of human MHC alleles that each have specific preferences for the peptide sequences they will ...frequency in the target population, as ... Voir le document complet

182

Efficient FPGA-Based Inference Architectures for Deep Learning Networks

Efficient FPGA-Based Inference Architectures for Deep Learning Networks

... used in the training, the validation and the testing processes of ...layers in order to reduce the computational resources ...accelerator for NNs, but it does not support variable network size ... Voir le document complet

117

Advances in deep learning with limited supervision and computational resources

Advances in deep learning with limited supervision and computational resources

... features In order to provide more quantitative intuitions on the learned discriminator at con- vergence, we adopt a proxy measure using discriminator ...features. Based on these fixed features, we train a ... Voir le document complet

139

Subject‐specific segregation of functional territories based on deep phenotyping

Subject‐specific segregation of functional territories based on deep phenotyping

... ground for individual functional atlasing, a novel analytic approach that requires a specific strategy to avoid circular reasoning, ...processes in cognition, deep phenotyping of behavioral ... Voir le document complet

53

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives

... els for Advanced Heart Disease and for Alcohol Abuse in Table 3 ...mentioned in the definition shown in Table 1 , such as ...“CABG” for Advanced Heart Disease for both ... Voir le document complet

20

Deep learning in event-based neuromorphic systems

Deep learning in event-based neuromorphic systems

... representations for event-based image recognition The type of features learned by a WTA-based algorithm are so-called one- hot ...that for a given input, only one or a few of the features will ... Voir le document complet

147

INPUT OF DEEP PHENOTYPING IN THE METABOLIC SYNDROME STRATIFICATION

INPUT OF DEEP PHENOTYPING IN THE METABOLIC SYNDROME STRATIFICATION

... role in multiple cellular processes. In recent years the mevalonate pathway has become a challenging and, in the meantime, fascinating topic, when a large number of experimental and clinical studies ... Voir le document complet

264

Towards a hybrid computational strategy based on Deep Learning for incompressible flows

Towards a hybrid computational strategy based on Deep Learning for incompressible flows

... solvers based on Machine Learning ...Dissayanake, for example, tested a simple four-layer MultiLayer Perceptron (MLP), to solve two benchmark problems, one of them being the Poisson equation with ... Voir le document complet

18

Computational Advantages of Deep Prototype-Based Learning

Computational Advantages of Deep Prototype-Based Learning

... activities in the M h1 maps of ...work in an on-line fashion, weights vectors of all layer are updated at the same time (in contrast to conventional deep architectures which require layer-wise ... Voir le document complet

8

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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks

... step in implementing an SNN is to encode the analog input data into the spike trains using either a rate based method [64], [15], some form of temporal coding [66], [67], or population coding ...neuron ... Voir le document complet

24

A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

... set, in the context of Deep Learning [2], Active Learning [13] and Reinforcement Learning ...Active learning, in the sense that we adapt our training strategy to the ... Voir le document complet

6

Structured priors for supervised learning in computational biology  

Structured priors for supervised learning in computational biology  

... showed in particular that the Tanimoto index, widely used in chemoinformatics, is a valid ...increase in expressiveness against loss in computational efficiency (Ramon and Gärtner, ... Voir le document complet

229

Review of Recent Deep Learning Based Methods for Image-Text Retrieval

Review of Recent Deep Learning Based Methods for Image-Text Retrieval

... Representation Learning (ARL) framework to learn modality-invariant representations for more effective image-text ...matching. In the ARL framework, a two-layer fully-connected network adversarial ... Voir le document complet

7

Deep Learning and Reinforcement Learning for Inventory Control

Deep Learning and Reinforcement Learning for Inventory Control

... works based on a MDP. Van Roy et al. (1997) presented a viable approach based on Neuro-Dynamic Programming (NDP) to solve inventory optimization including a ...reduction in the average inventory ... Voir le document complet

69

Deep learning-based approaches for depth and 6-DoF pose estimation

Deep learning-based approaches for depth and 6-DoF pose estimation

... In this thesis, we mainly focus on applying deep learning-based approaches to two geometric computer vision tasks: depth estimation on a single RGB image and 6-DoF[r] ... Voir le document complet

80

Towards Computational Design of 3D Scaffolds for Cell-Based Gene Therapy

Towards Computational Design of 3D Scaffolds for Cell-Based Gene Therapy

... PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. ... Voir le document complet

3

Deep Learning for Video Modelling

Deep Learning for Video Modelling

... are in the line of hope for sequences RNN-like approaches to this ...alternative for UCF101, with ...happening in 2D space applies in ...space in CNN become edges detectors. If ... Voir le document complet

90

Deep learning for cloud detection

Deep learning for cloud detection

... quired in the blue, green, red and near infrared wave- length ...images in order to reduce memory requirements, while the radiometric resolution is preserved at 12 ...corrected for radial dis- ... Voir le document complet

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