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Results for batch normalization

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13 papers · ranked by Valyu relevance

01
BbioRxiv20 Nov 2020
Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception
Mark R. Saddler, Ray Gonzalez, Josh H. McDermott
Perception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception…
89relevance
02
BbioRxiv17 Jul 2020
A correspondence between normalization strategies in artificial and biological neural networks
Yang Shen, Julia Wang, Saket Navlakha
A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods, such as batch normalization, weight normalization, and their many variants, help to stabilize…
85relevance
03
BbioRxiv2 Jun 2024
CytoBatchNorm: an R package with graphical interface for batch effects correction of cytometry data
Samuel Granjeaud, Naoill Abdellaoui, Anne-Sophie Chrétien, Eloise Woitrain + 8 more
Innovation in cytometry propelled it to an almost “omic” dimension technique during the last decade. The application fields concomitantly enlarged, resulting in generation of high-dimensional high-content data sets which have to be adequately designed, handled and analyzed. Experimental solutions and detailed data…
84relevance
04
BbioRxiv28 Sep 2022
Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies
Yilin Gao, Fengzhu Sun
Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with…
83relevance
05
BbioRxiv18 Oct 2022
Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images
Alexander Lin, Alex X. Lu
Data collected by high-throughput microscopy experiments are affected by batch effects, stemming from slight technical differences between experimental batches. Batch effects significantly impede machine learning efforts, as models learn spurious technical variation that do not generalize. We introduce batch effects…
83relevance
06
BbioRxiv19 Aug 2021
Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq Data
Benjamin R. Babcock, Astrid Kosters, Junkai Yang, Mackenzie L. White + 1 more
Single-cell RNA sequencing (scRNA-seq) can reveal accurate and sensitive RNA abundance in a single sample, but robust integration of multiple samples remains challenging. Large-scale scRNA-seq data generated by different workflows or laboratories can contain batch-specific systemic variation. Such variation challenges…
79relevance
07
BbioRxiv18 Oct 2023
Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity
Beibei Wang, Fengzhu Sun, Yihui Luan
The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods…
78relevance
08
BbioRxiv12 Sep 2019
Learning Divisive Normalization in Primary Visual Cortex
Max F. Burg, Santiago A. Cadena, George H. Denfield, Edgar Y. Walker + 3 more
Deep convolutional neural networks (CNNs) have emerged as the state of the art for predicting neural activity in visual cortex. While such models outperform classical linear-nonlinear and wavelet-based representations, we currently do not know what computations they approximate. Here, we tested divisive normalization…
78relevance
09
BbioRxiv15 Apr 2020
Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls
Michiel Bongaerts, Ramon Bonte, Serwet Demirdas, Ed H. Jacobs + 7 more
Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet…
77relevance
10
BbioRxiv17 Dec 2019
Simulation-based comprehensive study of batch effects in metabolomics studies
Miao Yu, Anna Roszkowska, Janusz Pawliszyn
Batch effects will influence the interpretation of metabolomics data. In order to avoid misleading results, batch effects should be corrected and normalized prior to statistical analysis. Metabolomics studies are usually performed without targeted compounds (e.g., internal standards) and it is a challenging task to…
77relevance
11
BbioRxiv29 Oct 2023
Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis
Beibei Wang, Yihui Luan
Significant advancements have been made in metagenomic research for the prediction of phenotypes based on microbiome data. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in…
75relevance
12
BbioRxiv12 Mar 2021
Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data
Alexis Vandenbon
Gene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate prediction of gene co-expression from such large datasets remain unclear. Especially…
72relevance
13
BbioRxiv19 Aug 2021
Modeling Neural Variability in Deep Networks with Dropout
Xu Pan, Ruben Coen-Cagli, Odelia Schwartz
Convolutional neural networks (CNNs) have been used to model the biological visual system. Compared to other models, CNNs can better capture neural responses to natural stimuli. However, previous successes are limited to modeling mean responses; while another fundamental aspect of cortical activity, namely response…
64relevance

Results for batch normalization

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

A correspondence between normalization strategies in artificial and biological neural networks

CytoBatchNorm: an R package with graphical interface for batch effects correction of cytometry data

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq Data

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

Learning Divisive Normalization in Primary Visual Cortex

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

Simulation-based comprehensive study of batch effects in metabolomics studies

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis

Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data

Modeling Neural Variability in Deep Networks with Dropout

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

A correspondence between normalization strategies in artificial and biological neural networks

CytoBatchNorm: an R package with graphical interface for batch effects correction of cytometry data

Batch Normalization Followed by Merging Is Powerful for Phenotype Prediction Integrating Multiple Heterogeneous Studies

Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq Data

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

Learning Divisive Normalization in Primary Visual Cortex

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

Simulation-based comprehensive study of batch effects in metabolomics studies

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis

Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data

Modeling Neural Variability in Deep Networks with Dropout