DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

Keywords

Deep learningComputer scienceProteomeThroughputArtificial neural networkSoftwareDeep neural networksProteomicsArtificial intelligenceIdentification (biology)Mass spectrometryInterference (communication)Software suiteMachine learningBioinformaticsChemistryChromatographyBiology

Affiliated Institutions

Related Publications

A Proteome-Scale Map of the Human Interactome Network

Thomas Rolland , Murat Taşan , Benoît Charloteaux +67 more

2014 Cell 1406 citations

TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data

Chengjie Chen , Hao Chen , Yi Zhang +4 more

2020 Molecular Plant 14052 citations

A survey on Image Data Augmentation for Deep Learning

Connor Shorten , Taghi M. Khoshgoftaar

Abstract Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfi...

2019 Journal Of Big Data 11041 citations

RolX

Keith Henderson , Brian Gallagher , Tina Eliassi‐Rad +6 more

Given a network, intuitively two nodes belong to the same role if they have similar structural behavior. Roles should be automatically determined from the data, and could be, fo...

2012 386 citations

Tissue-based map of the human proteome

Mathias Uhlén , Linn Fagerberg , Björn M. Hallström +33 more

Protein expression across human tissues Sequencing the human genome gave new insights into human biology and disease. However, the ultimate goal is to understand the dynamic exp...

2015 Science 14880 citations

Publication Info

Year: 2019
Type: article
Volume: 17
Issue: 1
Pages: 41-44
Citations: 2678
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2678

OpenAlex

Cite This

APA Style

                            
                                
                                    Vadim Demichev, 
                                
                                    Christoph B. Messner, 
                                
                                    Spyros I. Vernardis
                                
                                et al.
                            
                            (2019). 
                            DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. 
                            Nature Methods
                            , 17
                            (1)
                            , 41-44.
                            https://doi.org/10.1038/s41592-019-0638-x
                        

Identifiers

DOI: 10.1038/s41592-019-0638-x