DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis.

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Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

Communications Biology ◽

10.1038/s42003-021-02726-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Mingxuan Gao ◽

Wenxian Yang ◽

Chenxin Li ◽

Yuqing Chang ◽

Yachen Liu ◽

...

Keyword(s):

Data Analysis ◽

State Of The Art ◽

Data Driven ◽

High Coverage ◽

Discriminative Models ◽

Data Independent Acquisition ◽

Fragment Ions ◽

Comprehensive Information ◽

Spectral Libraries ◽

Negative Controls

AbstractWe developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

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Qualitative data analysis software: The state of the art

KWALON ◽

10.5117/2016.021.001.005 ◽

2016 ◽

Vol 21 (1) ◽

Author(s):

Susanne Friese

Keyword(s):

Data Analysis ◽

Qualitative Data ◽

Critical Appraisal ◽

State Of The Art ◽

The State ◽

Data Driven ◽

Analysis Software ◽

Qualitative Data Analysis ◽

New Developments ◽

Qualitative Data Analysis Software

Summary The aim of this paper is to provide an overview of the ‘state of the art of QDA or CAQDAS software. The author uses Kahneman’s ideas about slow and fast thinking as a framework. Slow thinking in the context of CAQDAS is related to researcher driven analysis and fast thinking to tool- and data driven analysis. The paper is divided into two parts. In the first part, the author describes trends and new developments and in the second part, she offers a critical appraisal.

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iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

Bioinformatics ◽

10.1093/bioinformatics/btz961 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2611-2613 ◽

Cited By ~ 5

Author(s):

Thang V Pham ◽

Alex A Henneman ◽

Connie R Jimenez

Keyword(s):

Open Source ◽

State Of The Art ◽

R Package ◽

Protein Quantification ◽

Supplementary Information ◽

Label Free ◽

Software Suite ◽

Data Independent Acquisition ◽

Free Data ◽

Acquisition Mode

Abstract Summary We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. Availability and implementation The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. Supplementary information Supplementary data are available at Bioinformatics online.

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Revisiting the Analysis of the Isochronous Mass Measurements of Uranium Fission Fragments at the ESR

EPJ Web of Conferences ◽

10.1051/epjconf/202022702012 ◽

2020 ◽

Vol 227 ◽

pp. 02012

Author(s):

R. S. Sidhu ◽

R. J. Chen ◽

Yu. A Litvinov ◽

Y. H. Zhang ◽

Keyword(s):

Experimental Data ◽

Data Analysis ◽

State Of The Art ◽

Fission Products ◽

Mass Measurements ◽

Fission Fragments ◽

Uranium Fission

The re-analysis of experimental data on mass measurements of ura- nium fission products obtained at the ESR in 2002 is discussed. State-of-the-art data analysis procedures developed for such measurements are employed.

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Data-driven deep density estimation

Neural Computing and Applications ◽

10.1007/s00521-021-06281-3 ◽

2021 ◽

Author(s):

Patrik Puchert ◽

Pedro Hermosilla ◽

Tobias Ritschel ◽

Timo Ropinski

Keyword(s):

Data Analysis ◽

Density Estimation ◽

Population Data ◽

Training Data ◽

Data Driven ◽

Discrete Observations ◽

Efficient Manner ◽

Continuous Models ◽

3D Scans ◽

Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

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A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2015.09.002 ◽

2015 ◽

Vol 122 (3) ◽

pp. 362-371 ◽

Cited By ~ 15

Author(s):

Yuhu Shi ◽

Weiming Zeng ◽

Nizhuan Wang ◽

Dongtailang Chen

Keyword(s):

Data Analysis ◽

Data Driven ◽

Analysis Method ◽

Data Analysis Method ◽

Group Data

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Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis

Energies ◽

10.3390/en14092371 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2371

Author(s):

Matthieu Dubarry ◽

David Beck

Keyword(s):

Cobalt Oxide ◽

State Of The Art ◽

Data Driven ◽

Nickel Aluminum ◽

Li Ion Battery ◽

Diagnosis And Prognosis ◽

Detailed Statistical Analysis ◽

Li Ion ◽

Synthetic Datasets ◽

Nickel Manganese

The development of data driven methods for Li-ion battery diagnosis and prognosis is a growing field of research for the battery community. A big limitation is usually the size of the training datasets which are typically not fully representative of the real usage of the cells. Synthetic datasets were proposed to circumvent this issue. This publication provides improved datasets for three major battery chemistries, LiFePO4, Nickel Aluminum Cobalt Oxide, and Nickel Manganese Cobalt Oxide 811. These datasets can be used for statistical or deep learning methods. This work also provides a detailed statistical analysis of the datasets. Accurate diagnosis as well as early prognosis comparable with state of the art, while providing physical interpretability, were demonstrated by using the combined information of three learnable parameters.

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Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽

10.3390/s20102778 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2778 ◽

Cited By ~ 12

Author(s):

Mohsen Azimi ◽

Armin Eslamlou ◽

Gokhan Pekcan

Keyword(s):

Deep Learning ◽

Structural Health Monitoring ◽

Health Monitoring ◽

High Speed ◽

Deep Neural Networks ◽

State Of The Art ◽

Data Driven ◽

Structural Health ◽

Promising Tool ◽

Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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