MaSS-Simulator: A highly configurable MS/MS simulator for generating test datasets for big data algorithms

2018 ◽  
Author(s):  
Muaaz Gul Awan ◽  
Fahad Saeed
Keyword(s):  
Mass Spectrometry ◽  
Big Data ◽  
Relative Error ◽  
Large Scale ◽  
Source Code ◽  
Ground Truth ◽  
Error Distribution ◽  
Challenging Problem ◽  
The Real ◽  
Integrity Testing

AbstractMass Spectrometry (MS) based proteomics has become an essential tool in the study of proteins. The big data from MS machines has led to the development of novel serial and parallel algorithmic tools. However, the absence of data benchmarks and ground truth makes the algorithmic integrity testing and reproducibility a challenging problem. To this end, we present MaSS-Simulator, which is an easy to use simulator and can be configured to generate MS/MS datasets for a wide variety of conditions with known ground truths. MaSS-Simulator offers a large number of configuration options to simulate control datasets with desired properties thus enabling rigorous and large scale algorithmic testing. We assessed 8,031 spectra generated by MaSS-Simulator by comparing them against the experimentally generated spectra of same peptides. Our results showed that MaSS-Simulator generated spectra were very close to the real-experimental spectra and had a relative-error distribution centered around 25%. In contrast the theoretical spectra for same peptides had relative-error distribution centered around 150%. Source code, executables and a user manual can be downloaded from https://github.com/pcdslab/MaSS-Simulator

scholarly journals Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6194-6201
Author(s):  
Jing Wang ◽  
Weiqing Min ◽  
Sujuan Hou ◽  
Shengnan Ma ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Image Recognition ◽  
Real World ◽  
Large Scale ◽  
Data Augmentation ◽  
Ground Truth ◽  
Classification Task ◽  
The Real ◽  
Product Recommendation ◽  
Contextual Advertising ◽  
Benchmark Datasets

Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset.

scholarly journals PolySTest: Robust statistical testing of proteomics data with missing values improves detection of biologically relevant features

2019 ◽  
Author(s):  
Veit Schwämmle ◽  
Christina E Hagensen ◽  
Adelina Rogowska-Wrzesinska ◽  
Ole N. Jensen
Keyword(s):  
Mass Spectrometry ◽  
Large Scale ◽  
Missing Values ◽  
Statistical Tests ◽  
Ground Truth ◽  
Statistical Testing ◽  
Molecular Networks ◽  
Proteomics Data ◽  
Biologically Relevant ◽  
Data Browsing

AbstractStatistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss Test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss Test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10%-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss Test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

scholarly journals PolySTest: Robust Statistical Testing of Proteomics Data with Missing Values Improves Detection of Biologically Relevant Features

2020 ◽  
Vol 19 (8) ◽  
pp. 1396-1408 ◽  
Author(s):  
Veit Schwämmle ◽  
Christina E. Hagensen ◽  
Adelina Rogowska-Wrzesinska ◽  
Ole N. Jensen
Keyword(s):  
Mass Spectrometry ◽  
Large Scale ◽  
Missing Values ◽  
Statistical Tests ◽  
Ground Truth ◽  
Statistical Testing ◽  
Molecular Networks ◽  
Proteomics Data ◽  
Biologically Relevant ◽  
Data Browsing

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10–20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

scholarly journals QOS MANAGEMENT IN REAL-TIME SPATIAL BIG DATA USING FEEDBACK CONTROL SCHEDULING

2015 ◽  
Vol II-3/W5 ◽  
pp. 243-248 ◽  
Author(s):  
S. Hamdi ◽  
E. Bouazizi ◽  
S. Faiz
Keyword(s):  
Big Data ◽  
Feedback Control ◽  
Real Time ◽  
Spatial Data ◽  
Large Scale ◽  
The Real ◽  
Remote Sensors ◽  
Spatial Big Data ◽  
Active Research ◽  
Access To Data

Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).

Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Complex Composition ◽  
Unified Protocol ◽  
Chromatography Mass Spectrometry ◽  
Marker Substances ◽  
Selection Testing ◽  
Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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