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Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

Nico Pfeifer, Andreas Leinenbach, Christian G Huber, Oliver Kohlbacher
2007 BMC Bioinformatics  
Results: We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems.  ...  We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase  ...  Acknowledgements We thank Marc Sturm for fruitful discussions on integrating our methods into OpenMS, and Andreas Bertsch and Torsten Blum for proofreading the manuscript.  ... 
doi:10.1186/1471-2105-8-468 pmid:18053132 pmcid:PMC2254445 fatcat:xzeis4gg3fb67k7pn6sgyqd6n4

Application of evolutionary algorithms to optimise one- and two-dimensional gradient chromatographic separations

Bram Huygens, Kyriakos Efthymiadis, Ann Nowé, Gert Desmet
2020 Journal of Chromatography A  
It was found that all three classes significantly outperform the plain grid search, especially in terms of the number of search runs needed to achieve a given separation quality.  ...  increasing difficulty of the separation problem.  ...  Acknowledgments: The authors thank MilliporeSigma for the continuous support. F.A. Franchina was funded by the FWO/FNRS Belgium EOS grant 30897864 "Chemical Information Mining in a Complex World".  ... 
doi:10.1016/j.chroma.2020.461435 pmid:32822975 fatcat:43gcy57envdgnaywtb6zinhqoi

OpenMS – An open-source software framework for mass spectrometry

Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert, Oliver Kohlbacher
2008 BMC Bioinformatics  
The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research.  ...  Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics.  ...  Finally, we would like tothank all the students who contributed to the project as part of their Bachelor and Master theses.  ... 
doi:10.1186/1471-2105-9-163 pmid:18366760 pmcid:PMC2311306 fatcat:ydzwiuwdhfff5k2xj4iaglslde

LC-MSsim - a simulation software for Liquid Chromatography Mass Spectrometry data

Ole Schulz-Trieglaff, Nico Pfeifer, Clemens Gropl, Oliver Kohlbacher, Knut Reinert
2008 BMC Bioinformatics  
In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time  ...  Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering.  ...  We thank Parag Mallick (UC Los Angeles) for providing us with the data sets for peptide detectability prediction.  ... 
doi:10.1186/1471-2105-9-423 pmid:18842122 pmcid:PMC2577660 fatcat:jqubcnbko5fczchifarnx3ccm4

Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

Qian Liu, Jing Lin, Li Wen, Shaozhou Wang, Peng Zhou, Li Mei, Shuyong Shang
2022 Frontiers in Genetics  
Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities  ...  In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner?  ...  Predicting Liquid Chromatographic Retention Times of Peptides from the Drosophila melanogaster Proteome by Machine Learning Approaches. Analytica Chim.  ... 
doi:10.3389/fgene.2021.800857 pmid:35096016 pmcid:PMC8795790 fatcat:3jdurp7r4necfivwyxtlxedu2e

CHICKN: extraction of peptide chromatographic elution profiles from large scale mass spectrometry data by means of Wasserstein compressive hierarchical cluster analysis

Olga Permiakova, Romain Guibert, Alexandra Kraut, Thomas Fortin, Anne-Marie Hesse, Thomas Burger
2021 BMC Bioinformatics  
In addition, we propose new kernels based on optimal transport, which interprets as intuitive similarity measures between chromatographic elution profiles.  ...  Results We propose a clustering algorithm that solves the powerful but computationally demanding kernel k-means objective function in a scalable way.  ...  Acknowledgements The authors thank Virginie Brun, Yohann Couté and Christophe Bruley for supports and fruitful discussions.  ... 
doi:10.1186/s12859-021-03969-0 pmid:33579189 fatcat:7ncqhn3vcvcenarg2n3idopjje

DIANA—algorithmic improvements for analysis of data-independent acquisition MS data

Johan Teleman, Hannes L Röst, George Rosenberger, Uwe Schmitt, Lars Malmström, Johan Malmström, Fredrik Levander
2014 Computer applications in the biosciences : CABIOS  
Results: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition  ...  Results: We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition  ...  ACKNOWLEDGEMENTS The authors thank Ufuk Kirik for the helpful discussions on the algorithms. Conflict of interest: none declared.  ... 
doi:10.1093/bioinformatics/btu686 pmid:25348213 fatcat:gc3puzdt6vbc5gbrxm3el2rqo4

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Salvatore Cappadona, Peter R. Baker, Pedro R. Cutillas, Albert J. R. Heck, Bas van Breukelen
2012 Amino Acids  
Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.  ...  Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade.  ...  We thank Elizabeth McClellan for her comments and discussion on the manuscript. Conflict of interest PC is advisor of Activiomics Ltd.  ... 
doi:10.1007/s00726-012-1289-8 pmid:22821268 pmcid:PMC3418498 fatcat:d2mslpel3far7f6pczk76vzj3e

Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC–MS for biomarker discovery

Christin Christin, Rainer Bischoff, Péter Horvatovich
2011 Talanta: The International Journal of Pure and Applied Analytical Chemistry  
Label-free quantitative LC-MS profiling of complex body fluids has become an important analytical tool for biomarker and biological knowledge discovery in the past decade.  ...  Finally, the review discusses the current state and trends in high throughput data processing and analysis solutions for users with little bioinformatics knowledge.  ...  Acknowledgements This work was part of the Bioassist and BioRange programs of the Netherlands Bioinformatics Centre (NBIC) and Gaining Momentum Initiatives of NBIC and the Netherlands Proteomics Center  ... 
doi:10.1016/j.talanta.2010.10.029 pmid:21215856 fatcat:crgg275t7vf2hpgwz2vfqewxvi

Quality assessment and interference detection in targeted mass spectrometry data using machine learning

Shadi Toghi Eshghi, Paul Auger, W Rodney Mathews
2018 Clinical Proteomics  
The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst.  ...  This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group  ...  Bill Forrest and Jean-Philippe Fortin for invaluable input on the statistical analysis in this study. Funding The study was funded by Genentech Inc., a member of the Roche group.  ... 
doi:10.1186/s12014-018-9209-x pmid:30323719 pmcid:PMC6173846 fatcat:t2lcjwstjfeuvgoss34xolbj7q

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data [article]

Sahar Iravani, Tim O.F. Conrad
2021 bioRxiv   pre-print
AbstractAnalyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise.  ...  We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying  ...  Acknowledgment This work was supported by the German Ministry for Education and Research (BMBF) as Berlin Big Data Center (01IS14013A) and the Berlin Center for Machine Learning (01IS18037I) and within  ... 
doi:10.1101/2021.02.19.431935 fatcat:lwyi6lyxh5fndocyagzphsztcq

Postgenomics: Proteomics and Bioinformatics in Cancer Research

Halima Bensmail, Abdelali Haoudi
2003 Journal of Biomedicine and Biotechnology  
Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward  ...  The study of the complete protein complement of the genome, the "proteome," referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be  ...  For cases in which no linear separation is possible, they can work in combination with the technique of "kernels" that automatically realizes a nonlinear mapping to a feature space.  ... 
doi:10.1155/s1110724303209207 pmid:14615629 pmcid:PMC514267 fatcat:gtwhtq6mynconcmuwfm7okdmqy

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Sahar Iravani, Tim O. F. Conrad
2022 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise.  ...  We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying  ...  Base peak chromatograms of the group with spike-in peptides are presented based on their mass-to-charge ration (m/z), retention time (RT), and ion charge.  ... 
doi:10.1109/tcbb.2022.3141656 pmid:35007196 fatcat:gxmz7bkheve7vdci6kr6imutze

On the feasibility of deep learning applications using raw mass spectrometry data

Joris Cadow, Matteo Manica, Roland Mathis, Tiannan Guo, Ruedi Aebersold, María Rodríguez Martínez
2021 Bioinformatics  
Summary In recent years, SWATH-MS has become the proteomic method of choice for data-independent–acquisition, as it enables high proteome coverage, accuracy and reproducibility.  ...  Using transfer learning to overcome sample sparsity, we exploit a collection of publicly available deep learning models already trained for the task of natural image classification.  ...  NAS), an automated machine learning structure for training new neural networks.  ... 
doi:10.1093/bioinformatics/btab311 pmid:34252933 fatcat:7m3fbz4wpzh6bh2q7jznlu5mnu

Biomarkers Discovery through Multivariate Statistical Methods: A Review of Recently Developed Methods and Applications in Proteomics

Elisa Robotti Marcello Manfredi
2013 Journal of Proteomics & Bioinformatics  
Here, we review the most recent applications of multivariate methods for the identification of biomarkers in proteomics with particular regard to the statistical methods exploited.  ...  Biomarkers discovery in proteomics is hampered by the use of high-throughput techniques providing a great number of candidates among which the true biomarkers have to be searched for.  ...  Dutkowski and Gambin [117] proposed a new approach to the biomarker selection problem: the approach is based on the application of several competing feature ranking procedures and compute a consensus  ... 
doi:10.4172/jpb.s3-003 fatcat:7t3cud2fbvblrdkmuy3s4vxjwu
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