scholarly journals Alpha-XIC: a deep neural network for scoring the coelution of peak groups improves peptide identification by data-independent acquisition mass spectrometry

2021 ◽  
Author(s):  
Jian Song ◽  
Changbin Yu
Keyword(s):  
Neural Network ◽  
Peptide Identification ◽  
Time Shift ◽  
Statistical Validation ◽  
Data Independent Acquisition ◽  
Fragment Ions ◽  
The Neural Network ◽  
Retention Time Shift ◽  
Peptide Fragment Ions ◽  
Validation Tool

ABSTRACTMotivationThe peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference.ResultsOn the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from identified peptides, Alpha-XIC is capable of reporting robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine, the ensuing statistical validation tool can update the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5h to 2h, Alpha-XIC delivered 16.7% ~ 49.1% improvements in the number of identified precursors at 1% FDR. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.6% and 13.8%, respectively, compared to the initial identification.Availability and ImplementationSource code are available at www.github.com/YuAirLab/Alpha-XIC.

scholarly journals Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

2021 ◽  
Author(s):  
Jian Song ◽  
Fangfei Zhang ◽  
Changbin Yu
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Deep Neural Network ◽  
Peptide Identification ◽  
Prediction Performance ◽  
Fragmentation Mechanism ◽  
Statistical Validation ◽  
Test Dataset ◽  
Data Independent Acquisition ◽  
Fragment Ions

ABSTRACTMotivationIdentification of peptides in data-independent acquisition (DIA) mass spectrometry (MS) typically relies on the scoring for the peak groups upon extracted chromatograms of fragment ions. Expanding fragment scoring features closer to the genuine experimental spectra can improve DIA identification. Deep learning is able to predict fragment presence without understanding the fragmentation mechanism that can enrich the scoring features in DIA identification.ResultsIn this work, we developed a deep neural network-based model, Alpha-Frag, to predict the fragment ions that should be present for a given peptide by reporting their probabilities of existence. The prediction performance was evaluated in terms of intersection over union (IoU), and Alpha-Frag achieved an average of >0.7 and outperformed substantially the benchmarks across the validation datasets. Furthermore, qualitative scores based on Alpha-Frag were designed and incorporated into the peptide statistical validation tools as auxiliary scores. Our preliminary experiments show that the qualitative scores by Alpha-Frag are profitable for DIA identification, especially in the case of short gradient, and yielded an increase of 10.1%-29.3% improvements for the test dataset compared to the same scoring strategy but using Prosit.Availability and ImplementationSource code and the trained model are available at www.github.com/YuAirLab/Alpha-Frag.

scholarly journals Evaluation of a Dual Isolation Width Acquisition (DIWA) method for isobaric labelling ratio decompression

2018 ◽  
Author(s):  
Theodoros I. Roumeliotis ◽  
Hendrik Weisser ◽  
Jyoti S. Choudhary
Keyword(s):  
Regression Models ◽  
Gene Knockout ◽  
Peptide Identification ◽  
Protein Quantification ◽  
Rational Approach ◽  
Linear Regression Models ◽  
Proteome Coverage ◽  
Fragment Ions ◽  
Isobaric Tagging ◽  
Peptide Fragment Ions

ABSRACTIsobaric labelling is a highly precise approach for protein quantification. However, due to the isolation interference problem, isobaric tagging suffers from ratio underestimation at the MS2 level. The use of narrow isolation widths is a rational approach to alleviate the interference problem; however, this approach compromises proteome coverage. We reasoned that although a very narrow isolation window will result in loss of peptide fragment ions, the reporter ion signals will be retained for a significant portion of the spectra. Based on this assumption we have designed a Dual Isolation Width Acquisition (DIWA) method, in which each precursor is first fragmented with HCD using a standard isolation width for peptide identification and preliminary quantification, followed by a second MS2 HCD scan using a much narrower isolation width for the acquisition of quantitative spectra with reduced interference. We leverage the quantification obtained by the “narrow” scans to build linear regression models and apply these to decompress the fold-changes measured at the “standard” scans. We evaluate the DIWA approach using a nested two species/gene knockout TMT-6plex experimental design and discuss the perspectives of this approach.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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