Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics

2020 ◽  
Vol 36 (12) ◽  
pp. 3781-3787
Author(s):  
Pavel Sulimov ◽  
Anastasia Voronkova ◽  
Attila Kertész-Farkas
Keyword(s):  
Neural Networks ◽  
Shotgun Proteomics ◽  
Mass Spectrometry Data ◽  
Stochastic Neural Networks ◽  
Restricted Boltzmann Machines ◽  
Score Functions ◽  
Boltzmann Machines ◽  
Discrimination Ability ◽  
False Discovery ◽  
Tandem Mass Spectrometry Data

Abstract Motivation The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions. Results Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4–6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark. Availability and implementation BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch. Contact [email protected] Supporting information Supplementary data are available at Bioinformatics online.

scholarly journals Combining high resolution and exact calibration to boost statistical power: A well-calibrated score function for high-resolution MS2 data

2018 ◽  
Author(s):  
Andy Lin ◽  
J. Jeffry Howbert ◽  
William Stafford Noble
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Statistical Power ◽  
State Of The Art ◽  
Score Function ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Mass Spectrometry Data ◽  
P Value ◽  
Score Functions

AbstractTo achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine’s scores are well calibrated—i.e., that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum—has proven to be challenging. Here, we describe a database search score function, the “residue evidence” (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a “combined p-value” score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p-value to the score functions used by several existing search engines. Our results suggest that the combined p-value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit (http://crux.ms).

scholarly journals Classifying a type of brain disorder in children: an effective fMRI based deep attempt

2021 ◽  
Vol 22 (1) ◽  
pp. 260
Author(s):  
Abeer M Mahmoud ◽  
Hanen Karamti
Keyword(s):  
Neural Networks ◽  
Children With Autism ◽  
Learning Approaches ◽  
Restricted Boltzmann Machines ◽  
Brain Disorder ◽  
Boltzmann Machines ◽  
Learning Framework ◽  
Straight Line ◽  
Healthy Control ◽  
The Brain

<span>Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The RBM focuses on obtaining the discriminative set of high guided features in the classification process, while the CA provides the regularization and the robustness of features for optimal objectives. The proposed framework diagnosed children with autism recording accuracy of 91, 14% and proved enhancement compared to literature.</span>

A transport equation approach for deep neural networks with quenched random weights

2021 ◽  
Author(s):  
Elena Agliari ◽  
Linda Albanese ◽  
Francesco Alemanno ◽  
Alberto Fachechi
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Symmetric Case ◽  
Hopfield Network ◽  
Equation Approach ◽  
Replica Symmetry ◽  
Replica Symmetry Breaking ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Random Weights

Abstract We consider a multi-layer Sherrington-Kirkpatrick spin-glass as a model for deep restricted Boltzmann machines with quenched random weights and solve for its free energy in the thermodynamic limit by means of Guerra's interpolating techniques under the RS and 1RSB ansatz. In particular, we recover the expression already known for the replica-symmetric case. Further, we drop the restriction constraint by introducing intra-layer connections among spins and we show that the resulting system can be mapped into a modular Hopfield network, which is also addressed via the same techniques up to the first step of replica symmetry breaking.

scholarly journals Preview: A Program for Surveying Shotgun Proteomics Tandem Mass Spectrometry Data

2011 ◽  
Vol 83 (13) ◽  
pp. 5259-5267 ◽  
Author(s):  
Yong J. Kil ◽  
Christopher Becker ◽  
Wendy Sandoval ◽  
David Goldberg ◽  
Marshall Bern
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Shotgun Proteomics ◽  
Mass Spectrometry Data ◽  
Tandem Mass ◽  
Tandem Mass Spectrometry Data

scholarly journals On the Training Algorithms for Restricted Boltzmann Machines

2019 ◽  
Author(s):  
Leandro Aparecido Passos ◽  
João Paulo Papa
Keyword(s):  
Feature Learning ◽  
Parameters Optimization ◽  
Fine Tuning ◽  
Stochastic Neural Networks ◽  
Heuristic Methods ◽  
Training Algorithms ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Heuristic Techniques ◽  
Temperature Parameter

Deep learning techniques have been studied extensively in the last years due to their good results related to essential tasks on a large range of applications, such as speech and face recognition, as well as object classification. Restrict Boltzmann Machines (RBMs) are among the most employed techniques, which are energy-based stochastic neural networks composed of two layers of neurons whose objective is to estimate the connection weights between them. Recently, the scientific community spent much effort on sampling methods since the effectiveness of RBMs is directly related to the success of such a process. Thereby, this work contributes to studies concerning different training algorithms for RBMs, as well as its variants Deep Belief Networks and Deep Boltzmann Machines. Further, the work covers the application of meta-heuristic methods concerning a proper fine-tune of these techniques. Moreover, the validation of the model is presented in the context of image reconstruction and unsupervised feature learning. In general, we present different approaches to training these techniques, as well as the evaluation of meta-heuristic methods for fine-tuning parameters, and its main contributions are: (i) temperature parameter introduction in DBM formulation, (ii) DBM using adaptive temperature, (iii) DBM meta-parameter optimization through meta-heuristic techniques, and (iv) infinity Restricted Boltzmann Machine (iRBM) meta-parameters optimization through meta-heuristic techniques.

scholarly journals Neuro-Symbolic Probabilistic Argumentation Machines

2020 ◽  
Author(s):  
Regis Riveret ◽  
Son Tran ◽  
Artur d'Avila Garcez
Keyword(s):  
Neural Networks ◽  
Machine Learning Techniques ◽  
Sampled Data ◽  
Symbolic System ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Learning Techniques ◽  
The Neural Networks ◽  
Probabilistic Argumentation ◽  
The Given

Neural-symbolic systems combine the strengths of neural networks and symbolic formalisms. In this paper, we introduce a neural-symbolic system which combines restricted Boltzmann machines and probabilistic semi-abstract argumentation. We propose to train networks on argument labellings explaining the data, so that any sampled data outcome is associated with an argument labelling. Argument labellings are integrated as constraints within restricted Boltzmann machines, so that the neural networks are used to learn probabilistic dependencies amongst argument labels. Given a dataset and an argumentation graph as prior knowledge, for every example/case K in the dataset, we use a so-called K-maxconsistent labelling of the graph, and an explanation of case K refers to a K-maxconsistent labelling of the given argumentation graph. The abilities of the proposed system to predict correct labellings were evaluated and compared with standard machine learning techniques. Experiments revealed that such argumentation Boltzmann machines can outperform other classification models, especially in noisy settings.

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