a joint deep learning model for simultaneous batch effect

A Joint Deep Learning Model for Simultaneous Batch Effect ...

Sep 25, 2020  Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN.

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A joint deep learning model enables simultaneous batch ...

May 25, 2021  Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN.

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A Joint Deep Learning Model for Simultaneous Batch Effect ...

Sep 25, 2020  A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering Sep 25, 2020 News Stories Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in

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A Joint Deep Learning Model for Simultaneous Batch Effect ...

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

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Altmetric – A joint deep learning model enables ...

A joint deep learning model enables simultaneous batch effect correction, denoising and clustering in single-cell transcriptomics Overview of attention for article published in Genome Research, May 2021

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Simultaneous deep generative modelling and clustering of ...

May 10, 2021  To evaluate whether scDEC can automatically correct or alleviate batch effects in the training process, we collected three types (CLP, LMPP and

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Joint Deep Learning for land cover and land use ...

Feb 01, 2019  The general workflow of the land cover (LC) and land use (LU) Joint Deep Learning (JDL). Essentially, the Joint Deep Learning (JDL) model has four key advantages: 1. The JDL is designed for joint land cover and land use classification in an automatic fashion, whereas previous methods can only classify a single, specific level of representation.

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A multi-model deep convolutional neural network for ...

Mar 01, 2020  Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data.

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Removal of batch effects using distribution-matching ...

To the best of our knowledge, MMD nets have not been applied to the problem of removal of batch effects, which is considered here. 6 Conclusions and future research. We presented a novel deep learning approach for non-linear removal of batch effects, based on residual networks, to match the distributions of source and target samples.

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Effect of Batch Size on Neural Net Training by Daryl ...

May 24, 2020  Figure 2: Stochastic gradient descent update equation. Adapted from Keskar et al [1]. B_k is a batch sampled from the training dataset, and its size can vary from 1 to m (the total number of ...

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A Joint Deep Learning Model for Simultaneous Batch Effect ...

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

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Simultaneous deep generative modelling and clustering of ...

May 10, 2021  To evaluate whether scDEC can automatically correct or alleviate batch effects in the training process, we collected three types (CLP, LMPP and

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A cyclical deep learning based framework for simultaneous ...

Nov 10, 2020  So, S., Mun, J. Rho, J. Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using

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An integrated deep learning framework for joint ...

May 01, 2020  The main components of a deep learning network are convolutional layers, the formula of a convolutional layer is (1) a n l + 1 = f (∑ m = 1 M W n m l * a m l + b n l + 1) where a n l + 1 and a m l represent the nth feature map in the (l+1)th layer and the mth feature map in the lth layer, respectively, and M denotes the number of feature maps ...

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A multi-model deep convolutional neural network for ...

Mar 01, 2020  Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data.

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Removal of batch effects using distribution-matching ...

To the best of our knowledge, MMD nets have not been applied to the problem of removal of batch effects, which is considered here. 6 Conclusions and future research. We presented a novel deep learning approach for non-linear removal of batch effects, based on residual networks, to match the distributions of source and target samples.

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Time-driven feature-aware jointly deep reinforcement ...

Feb 01, 2020  Accordingly, the accuracy, adaptability and interpretability of the deep learning perception model are improved via the consideration of “feature” and “temporal” aspects. Afterwards, we designed a joint framework to jointly construct and iteratively train the supervised deep learning model and the reinforcement learning model.

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Dropout imputation and batch effect correction for single ...

Traditional batch effect correction methods, for example limma and ComBat, are mainly based on linear regression, where batch effects are modeled either as known variables and regressed out from the raw joint data matrix. These methods have proven to be valuable in correcting batch effects for

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Removal of Batch Effects using Distribution-Matching ...

Oct 13, 2016  We presented a novel deep learning approach for non-linear removal of batch effects, based on residual networks, to match the distributions of the source and target samples. We applied our approach to CyTOF and scRNA-seq and demonstrated impressive performance. To the best of our knowledge, such a performance on CyTOF data was never reported.

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Simultaneous Deep Stereo Matching and Dehazing with ...

Jan 21, 2020  Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and ...

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A deep learning method for online capacity estimation of ...

Oct 01, 2019  The verification results demonstrate that the proposed deep learning method achieves promising accuracy in the capacity estimation, indicating that our method is an efficient tool for online health management of Li-ion batteries. Based on this study, the proposed deep learning method has shown plausible benefits for battery capacity estimations.

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Deep learning for symbols detection and classification in ...

Sep 01, 2020  Deep Learning (DL) (Goodfellow, ... First, it is a simple framework, which allows simultaneous predictions of multiple bounding boxes and class probabilities using a single convolutional neural network. ... The network was trained with a learning rate of 0.001 and training is stopped when the model was trained on 10,000 batches, (batch size of 64).

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Deep generative model embedding of single-cell RNA-Seq ...

May 05, 2021  As a deep-learning model trained by mini-batch stochastic gradient descent, scPhere is especially suited to process large scRNA-seq datasets with complex multilevel batch effects

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A Deep Learning Approach for Maximum Activity Links in D2D ...

Jul 03, 2019  Due to the fact that the performance of a deep learning model is greatly influenced by the model parameters, it is difficult to train a deep learning network . For instance, in presence of a limited data set, if the training model is designed in a way that is too complicated and tries to approximate a complex data relationship by using a noise ...

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A cyclical deep learning based framework for simultaneous ...

Nov 10, 2020  So, S., Mun, J. Rho, J. Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using

get price

An integrated deep learning framework for joint ...

May 01, 2020  The main components of a deep learning network are convolutional layers, the formula of a convolutional layer is (1) a n l + 1 = f (∑ m = 1 M W n m l * a m l + b n l + 1) where a n l + 1 and a m l represent the nth feature map in the (l+1)th layer and the mth feature map in the lth layer, respectively, and M denotes the number of feature maps ...

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Time-driven feature-aware jointly deep reinforcement ...

Feb 01, 2020  Accordingly, the accuracy, adaptability and interpretability of the deep learning perception model are improved via the consideration of “feature” and “temporal” aspects. Afterwards, we designed a joint framework to jointly construct and iteratively train the supervised deep learning model and the reinforcement learning model.

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Deep Learning Approach for Evaluating Knee MR Images ...

Jul 31, 2018  The proposed deep learning approach achieved high diagnostic accuracy for detecting cartilage lesions within the knee joint, with AUCs above 0.91. Furthermore, the sensitivity and specificity of the cartilage lesion detection system were comparable to the diagnostic performance of clinical radiologists, including radiology residents ...

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Removal of Batch Effects using Distribution-Matching ...

Oct 13, 2016  We presented a novel deep learning approach for non-linear removal of batch effects, based on residual networks, to match the distributions of the source and target samples. We applied our approach to CyTOF and scRNA-seq and demonstrated impressive performance. To the best of our knowledge, such a performance on CyTOF data was never reported.

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Simultaneous Visual and Linguistic Embeddings with CNNs ...

3.2 Model Architecture Figure 1: A schematic of the learning system. Figure 1 shows an overview of the complete learning system. The system performs a forward pass on a batch of several pairs of images and captions at once. Each sentence (arranged in a dependency tree) is

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The challenge of simultaneous object detection and pose ...

We introduce three novel deep learning architectures for the problem of simultaneous object detection and pose estimation. Our models seek to perform a joint detection and pose estimation, trained fully end-to-end. We start with a model that fully integrates the tasks of

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Deep learning for symbols detection and classification in ...

Sep 01, 2020  Deep Learning (DL) (Goodfellow, ... First, it is a simple framework, which allows simultaneous predictions of multiple bounding boxes and class probabilities using a single convolutional neural network. ... The network was trained with a learning rate of 0.001 and training is stopped when the model was trained on 10,000 batches, (batch size of 64).

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Spatio-temporal deep learning models for tip force ...

May 30, 2019  We study different deep learning approaches to facilitate calibration between this spatio-temporal image data and the related forces. In particular, we propose a novel convGRU-CNN architecture for simultaneous spatial and temporal data processing. The needle can be adapted to different operating ranges by changing the stiffness of the epoxy layer.

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A deep learning method for online capacity estimation of ...

Oct 01, 2019  The verification results demonstrate that the proposed deep learning method achieves promising accuracy in the capacity estimation, indicating that our method is an efficient tool for online health management of Li-ion batteries. Based on this study, the proposed deep learning method has shown plausible benefits for battery capacity estimations.

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Is it more cost effective to buy a graphics card locally ...

If you are serious about your work related to Deep Learning, it’s best if you buy a graphics card as it is faster(assuming you have a good graphics card like GTX ...

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Deep Hashing: A Joint Approach for Image Signature ...

Aug 12, 2016  Following the common practice in deep learning, two groups of quantities ∂ Q / ∂ \w k, k = 1 ⋯ K and ∂ Q / ∂ \z i (i. ranges over the index set of current mini-batch) need to be estimated on the hashing loss layer at each iteration. The former group of quantities are used for updating

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Segmentation-Based Deep-Learning Approach for Surface ...

Mar 20, 2019  Segmentation-Based Deep-Learning Approach for Surface-Defect Detection. 03/20/2019 ∙ by Domen Tabernik, et al. ∙ University of Ljubljana ∙ 16 ∙ share . Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection.

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From deep learning to transfer learning for the prediction ...

Dec 14, 2018  Recently, a deep learning model has been successfully developed to estimate the stress distributions in the aorta structure . This approach is also applied on bone tissue [ 44 ]. Other real-world applications include the study of electronic health records data [ 45 ], the development of biological networks [ 46 ], or the prediction of ...

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