IMass Time: The Future, in Future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass.

[1]  Elena Botts Jj , 2018, Dictionary of Agriculture.

[2]  Kwanjeera Wanichthanarak,et al.  Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine , 2018, Omics : a journal of integrative biology.

[3]  X. Guo,et al.  The changes of immunoglobulin G N-glycosylation in blood lipids and dyslipidaemia , 2018, Journal of Translational Medicine.

[4]  Wangwei,et al.  Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery. , 2018, Omics : a journal of integrative biology.

[5]  Youxin Wang,et al.  Ischemic stroke is associated with the pro-inflammatory potential of N-glycosylated immunoglobulin G , 2018, Journal of Neuroinflammation.

[6]  Tingli Su,et al.  State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence , 2018 .

[7]  S. Laws,et al.  Unravelling Immunoglobulin G Fc N-Glycosylation: A Dynamic Marker Potentiating Predictive, Preventive and Personalised Medicine , 2018, International journal of molecular sciences.

[8]  Vural Özdemir,et al.  Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, "The Internet of Things" and Next-Generation Technology Policy. , 2018, Omics : a journal of integrative biology.

[9]  Naoyuki Taniguchi,et al.  N-glycan and Alzheimer's disease. , 2017, Biochimica et biophysica acta. General subjects.

[10]  Youxin Wang,et al.  Serum peptidome profiling for the diagnosis of colorectal cancer: discovery and validation in two independent cohorts , 2017, Oncotarget.

[11]  A. Lusis,et al.  Multi-omics approaches to disease , 2017, Genome Biology.

[12]  I. Rudan,et al.  The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson's disease , 2017, Glycobiology.

[13]  Xiaomin Ying,et al.  Quantitative proteomics by SWATH-MS reveals sophisticated metabolic reprogramming in hepatocellular carcinoma tissues , 2017, Scientific Reports.

[14]  Eric Adua,et al.  Innovation Analysis on Postgenomic Biomarkers: Glycomics for Chronic Diseases. , 2017, Omics : a journal of integrative biology.

[15]  P. Hamet,et al.  Artificial intelligence in medicine. , 2017, Metabolism: Clinical and Experimental.

[16]  C. McCudden The future of artificial intelligence and interpretative specialization in clinical biochemistry. , 2017, Clinical biochemistry.

[17]  Paolo Cifani,et al.  ProteoModlR for functional proteomic analysis , 2017, BMC Bioinformatics.

[18]  RaiSneha,et al.  Novel Lipidomic Biomarkers in Hyperlipidemia and Cardiovascular Diseases: An Integrative Biology Analysis. , 2017 .

[19]  F. Chen,et al.  Screening for potential serum‐based proteomic biomarkers for human type 2 diabetes mellitus using MALDI‐TOF MS , 2017, Proteomics. Clinical applications.

[20]  R. Matthiesen,et al.  Bronchoalveolar Lavage Proteomics in Patients with Suspected Lung Cancer , 2017, Scientific Reports.

[21]  Carlo Combi,et al.  Editorial from the new Editor-in-Chief: Artificial Intelligence in Medicine and the forthcoming challenges , 2017, Artif. Intell. Medicine.

[22]  Tao Zhang,et al.  SWATH-based proteomics identified carbonic anhydrase 2 as a potential diagnosis biomarker for nasopharyngeal carcinoma , 2017, Scientific Reports.

[23]  W. Jia,et al.  Serum lipid alterations identified in chronic hepatitis B, hepatitis B virus-associated cirrhosis and carcinoma patients , 2017, Scientific Reports.

[24]  W. Lehmann A timeline of stable isotopes and mass spectrometry in the life sciences. , 2017, Mass spectrometry reviews.

[25]  S. Sethi,et al.  Recent advances in lipidomics: Analytical and clinical perspectives. , 2017, Prostaglandins & other lipid mediators.

[26]  Feng Zhu,et al.  Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis , 2016, Scientific Reports.

[27]  M. Farag,et al.  A Comparative Metabolomics Approach Reveals Early Biomarkers for Metabolic Response to Acute Myocardial Infarction , 2016, Scientific Reports.

[28]  Q. Zhan,et al.  Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging , 2016, Scientific Reports.

[29]  S. Wild,et al.  A Case-control Study in an Orcadian Population Investigating the Relationship between Human Plasma N-glycans and Metabolic Syndrome , 2016 .

[30]  G. Vidarsson,et al.  MALDI-TOF-MS reveals differential N-linked plasma- and IgG-glycosylation profiles between mothers and their newborns , 2016, Scientific Reports.

[31]  Ruedi Aebersold,et al.  Mass-spectrometric exploration of proteome structure and function , 2016, Nature.

[32]  Xiaojiao Zheng,et al.  Metabolomics Profiling for Obstructive Sleep Apnea and Simple Snorers , 2016, Scientific Reports.

[33]  M. Larsen,et al.  Glycomic analysis of gastric carcinoma cells discloses glycans as modulators of RON receptor tyrosine kinase activation in cancer. , 2016, Biochimica et biophysica acta.

[34]  Richard D. Smith,et al.  Advances in targeted proteomics and applications to biomedical research , 2016, Proteomics.

[35]  S. Nishimura,et al.  Glycoblotting method allows for rapid and efficient glycome profiling of human Alzheimer's disease brain, serum and cerebrospinal fluid towards potential biomarker discovery. , 2016, Biochimica et biophysica acta.

[36]  I. Rudan,et al.  Profiling IgG N-glycans as potential biomarker of chronological and biological ages , 2016, Medicine.

[37]  Xiuhua Guo,et al.  Glycan Biomarkers for Rheumatoid Arthritis and Its Remission Status in Han Chinese Patients. , 2016, Omics : a journal of integrative biology.

[38]  Anthony J. Cesnik,et al.  Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation. , 2016, Annual review of analytical chemistry.

[39]  Robert Mistrik,et al.  Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics , 2016, International journal of molecular sciences.

[40]  P. Rudd,et al.  Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes , 2016, Theranostics.

[41]  Youping Deng,et al.  Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions , 2016, Oncotarget.

[42]  D. Meierhofer,et al.  Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology , 2016, International journal of molecular sciences.

[43]  Kannan Rangiah,et al.  A method for comparative metabolomics in urine using high resolution mass spectrometry. , 2016, Journal of chromatography. A.

[44]  I. Rudan,et al.  The Association Between Glycosylation of Immunoglobulin G and Hypertension , 2016, Medicine.

[45]  Abdellah Tebani,et al.  Optimization of a liquid chromatography ion mobility-mass spectrometry method for untargeted metabolomics using experimental design and multivariate data analysis. , 2016, Analytica chimica acta.

[46]  Z. Ouyang,et al.  Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment. , 2016, Analytical chemistry.

[47]  N. Vaziri,et al.  An integrated lipidomics and metabolomics reveal nephroprotective effect and biochemical mechanism of Rheum officinale in chronic renal failure , 2016, Scientific Reports.

[48]  Lunzhao Yi,et al.  Serum Metabolic Profiling Reveals Altered Metabolic Pathways in Patients with Post-traumatic Cognitive Impairments , 2016, Scientific Reports.

[49]  Houbing Song,et al.  Internet of Things and Big Data Analytics for Smart and Connected Communities , 2016, IEEE Access.

[50]  Xin Lu,et al.  Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer , 2016, Scientific Reports.

[51]  J. Rich Cancer stem cells: master gatekeepers and regulators of cancer growth and metastasis Introduction , 2016, Medicine.

[52]  Otto Savolainen,et al.  A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry. , 2016, Journal of proteome research.

[53]  Allison Doerr Global metabolomics , 2016, Nature Methods.

[54]  Eric Adua,et al.  Glycomics and its application potential in precision medicine , 2016 .

[55]  C. Barbas,et al.  Multiplatform metabolomic fingerprinting as a tool for understanding hypercholesterolemia in Wistar rats , 2016, European Journal of Nutrition.

[56]  Magnus Palmblad,et al.  MassyTools: A High-Throughput Targeted Data Processing Tool for Relative Quantitation and Quality Control Developed for Glycomic and Glycoproteomic MALDI-MS. , 2015, Journal of proteome research.

[57]  Ying-yong Zhao,et al.  Lipidomics: Novel insight into the biochemical mechanism of lipid metabolism and dysregulation-associated disease. , 2015, Chemico-biological interactions.

[58]  P. Urban,et al.  Automated on-line liquid-liquid extraction system for temporal mass spectrometric analysis of dynamic samples. , 2015, Analytica chimica acta.

[59]  G. Omenn,et al.  Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project. , 2015, Journal of proteome research.

[60]  M. A. Lasunción,et al.  Quantitative lipidomic analysis of plasma and plasma lipoproteins using MALDI-TOF mass spectrometry. , 2015, Chemistry and physics of lipids.

[61]  Michael S Bereman,et al.  Data-independent-acquisition mass spectrometry for identification of targeted-peptide site-specific modifications , 2015, Analytical and Bioanalytical Chemistry.

[62]  David S. Wishart,et al.  MetaboAnalyst 3.0—making metabolomics more meaningful , 2015, Nucleic Acids Res..

[63]  A. Lamond,et al.  Multidimensional proteomics for cell biology , 2015, Nature Reviews Molecular Cell Biology.

[64]  Felix Wortmann,et al.  Internet of Things , 2015, Business & Information Systems Engineering.

[65]  R. Testa,et al.  N-Glycomic Changes in Serum Proteins in Type 2 Diabetes Mellitus Correlate with Complications and with Metabolic Syndrome Parameters , 2015, PloS one.

[66]  Miguel Rocha,et al.  An Integrated Computational Platform for Metabolomics Data Analysis , 2015, PACBB.

[67]  Alexander Schmidt,et al.  Evaluation of data-dependent and -independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites. , 2014, Journal of proteome research.

[68]  Augustin Scalbert,et al.  Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine. , 2014, Analytical chemistry.

[69]  Guan-Yuan Chen,et al.  Quantification of target analytes in various biofluids using a postcolumn infused-internal standard method combined with matrix normalization factors in liquid chromatography-electrospray ionization mass spectrometry. , 2014, Journal of chromatography. A.

[70]  Ian D Wilson,et al.  LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. , 2014, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[71]  I. Rudan,et al.  Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research , 2014, Molecular & Cellular Proteomics.

[72]  D. French,et al.  Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Assay to Quantify Plasma Busulfan , 2014, Therapeutic drug monitoring.

[73]  Eivind Hovig,et al.  From proteomes to complexomes in the era of systems biology , 2014, Proteomics.

[74]  Yuan Tian,et al.  Glycoproteomic and glycomic databases , 2014, Clinical Proteomics.

[75]  Pat Sandra,et al.  Lipidomics from an analytical perspective. , 2013, Current opinion in chemical biology.

[76]  J. Gal A History of European Mass Spectrometry , 2013, Journal of The American Society for Mass Spectrometry.

[77]  Cristiano Castelfranchi,et al.  Alan Turing’s “Computing Machinery and Intelligence” , 2013 .

[78]  Alexander Goesmann,et al.  MeltDB 2.0–advances of the metabolomics software system , 2013, Bioinform..

[79]  A. Konijnenberg,et al.  Native ion mobility-mass spectrometry and related methods in structural biology. , 2013, Biochimica et biophysica acta.

[80]  R. Moritz,et al.  Current algorithmic solutions for peptide-based proteomics data generation and identification. , 2013, Current opinion in biotechnology.

[81]  Tomislav Horvat,et al.  Glycomics meets genomics, epigenomics and other high throughput omics for system biology studies. , 2013, Current opinion in chemical biology.

[82]  Haixu Tang,et al.  Software tools for glycan profiling. , 2013, Methods in molecular biology.

[83]  Yassene Mohammed,et al.  Cloud parallel processing of tandem mass spectrometry based proteomics data. , 2012, Journal of proteome research.

[84]  Kurt Widhalm,et al.  Targeted profiling of atherogenic phospholipids in human plasma and lipoproteins of hyperlipidemic patients using MALDI-QIT-TOF-MS/MS. , 2012, Atherosclerosis.

[85]  François Bouzom,et al.  Quantitative mass spectrometry imaging of propranolol and olanzapine using tissue extinction calculation as normalization factor. , 2012, Journal of proteomics.

[86]  Youxin Wang,et al.  Profiling Plasma Peptides for the Identification of Potential Ageing Biomarkers in Chinese Han Adults , 2012, PloS one.

[87]  N. Packer,et al.  Structural analysis of N- and O-glycans released from glycoproteins , 2012, Nature Protocols.

[88]  David S. Wishart,et al.  MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis , 2012, Nucleic Acids Res..

[89]  G. Siuzdak,et al.  Innovation: Metabolomics: the apogee of the omics trilogy , 2012, Nature Reviews Molecular Cell Biology.

[90]  Oscar Yanes,et al.  Metabolomics: the apogee of the omics trilogy , 2012 .

[91]  Edward L. Huttlin,et al.  Systematic and quantitative assessment of the ubiquitin-modified proteome. , 2011, Molecular cell.

[92]  I. Rudan,et al.  Suboptimal health: a potential preventive instrument for non-communicable disease control and management: Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations , 2012 .

[93]  Stefan R Bornstein,et al.  Shotgun lipidomics on high resolution mass spectrometers. , 2011, Cold Spring Harbor perspectives in biology.

[94]  Joshua D. Knowles,et al.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.

[95]  I. Rudan,et al.  Glycomics meets lipidomics--associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations. , 2011, Molecular bioSystems.

[96]  B. McManus,et al.  The Human Serum Metabolome , 2011, PloS one.

[97]  Gerhard G. Thallinger,et al.  Lipid Data Analyzer: unattended identification and quantitation of lipids in LC-MS data , 2011, Bioinform..

[98]  Martin Frank,et al.  EUROCarbDB: An open-access platform for glycoinformatics , 2010, Glycobiology.

[99]  Martin Frank,et al.  GlycomeDB—a unified database for carbohydrate structures , 2010, Nucleic Acids Res..

[100]  Etienne Carbonnelle,et al.  MALDI-TOF mass spectrometry tools for bacterial identification in clinical microbiology laboratory. , 2011, Clinical biochemistry.

[101]  C. Overall,et al.  Protease specificity profiling by tandem mass spectrometry using proteome-derived peptide libraries. , 2011, Methods in molecular biology.

[102]  Royston Goodacre,et al.  Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. , 2011, Chemical Society reviews.

[103]  D. Raoult,et al.  MALDI-TOF-mass spectrometry applications in clinical microbiology. , 2010, Future microbiology.

[104]  Wei Wang,et al.  Genomics Meets Glycomics—The First GWAS Study of Human N-Glycome Identifies HNF1α as a Master Regulator of Plasma Protein Fucosylation , 2010, PLoS genetics.

[105]  Pauline M Rudd,et al.  Ultra performance liquid chromatographic profiling of serum N-glycans for fast and efficient identification of cancer associated alterations in glycosylation. , 2010, Analytical chemistry.

[106]  P. Pevzner,et al.  Deconvolution and Database Search of Complex Tandem Mass Spectra of Intact Proteins , 2010, Molecular & Cellular Proteomics.

[107]  G. Nolan,et al.  Computational solutions to large-scale data management and analysis , 2010, Nature Reviews Genetics.

[108]  A. Shevchenko,et al.  Lipidomics: coming to grips with lipid diversity , 2010, Nature Reviews Molecular Cell Biology.

[109]  Matej Oresic,et al.  MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.

[110]  B. Kuster,et al.  Proteomics: a pragmatic perspective , 2010, Nature Biotechnology.

[111]  S. Blanksby,et al.  Advances in mass spectrometry for lipidomics. , 2010, Annual review of analytical chemistry.

[112]  David S. Wishart,et al.  MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data , 2010, Nucleic Acids Res..

[113]  A. Kontush,et al.  Lipidomics as a Tool for the Study of Lipoprotein Metabolism , 2010, Current atherosclerosis reports.

[114]  Matthew P Campbell,et al.  GlycoExtractor: a web-based interface for high throughput processing of HPLC-glycan data. , 2010, Journal of proteome research.

[115]  Juhnyoung Lee,et al.  A view of cloud computing , 2010, CACM.

[116]  Thomas Hankemeier,et al.  Analytical strategies in lipidomics and applications in disease biomarker discovery. , 2009, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[117]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[118]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[119]  Thomas Sandholm,et al.  What's inside the Cloud? An architectural map of the Cloud landscape , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[120]  Peter Szolovits,et al.  The coming of age of artificial intelligence in medicine , 2009, Artif. Intell. Medicine.

[121]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[122]  V. Gault,et al.  Understanding Bioanalytical Chemistry: Principles and Applications , 2009 .

[123]  Ken S Lau,et al.  N-Glycans in cancer progression. , 2008, Glycobiology.

[124]  Alessio Ceroni,et al.  GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. , 2008, Journal of proteome research.

[125]  G. Meer,et al.  Membrane lipids: where they are and how they behave , 2008, Nature Reviews Molecular Cell Biology.

[126]  H. Perreault,et al.  A 2,5-dihydroxybenzoic acid/N,N-dimethylaniline matrix for the analysis of oligosaccharides by matrix-assisted laser desorption/ionization mass spectrometry. , 2007, Rapid communications in mass spectrometry : RCM.

[127]  M. Nishimura,et al.  Analysis of N-glycan in serum glycoproteins from db/db mice and humans with type 2 diabetes. , 2007, American journal of physiology. Endocrinology and metabolism.

[128]  M. Molinari,et al.  In and out of the ER: protein folding, quality control, degradation, and related human diseases. , 2007, Physiological reviews.

[129]  I. Hargittai Gerald E. Brown and Chang-Hwan Lee, Hans Bethe and His Physics , 2007 .

[130]  K. Downard Francis William Aston: The Man Behind the Mass Spectrograph , 2007, European journal of mass spectrometry.

[131]  Naoyuki Taniguchi,et al.  Comparison of the methods for profiling glycoprotein glycans--HUPO Human Disease Glycomics/Proteome Initiative multi-institutional study. , 2007, Glycobiology.

[132]  J. German,et al.  Lipidomics and lipid profiling in metabolomics , 2007, Current opinion in lipidology.

[133]  Terence C W Poon,et al.  Opportunities and limitations of SELDI-TOF-MS in biomedical research: practical advices , 2007, Expert review of proteomics.

[134]  A. Makarov,et al.  Orbitrap Mass Analyzer – Overview and Applications in Proteomics , 2006, Proteomics.

[135]  R. Cooks,et al.  New surfaces for desorption electrospray ionization mass spectrometry: porous silicon and ultra-thin layer chromatography plates. , 2006, Rapid communications in mass spectrometry : RCM.

[136]  Kai Simons,et al.  Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. , 2006, Analytical chemistry.

[137]  Martin Frank,et al.  GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research. , 2006, Glycobiology.

[138]  Zheng Ouyang,et al.  Ambient Mass Spectrometry , 2006, Science.

[139]  H. Lai,et al.  SELDI-TOF MS profiling of plasma proteins in ovarian cancer. , 2006, Taiwanese journal of obstetrics & gynecology.

[140]  A. Makarov,et al.  Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. , 2006, Analytical chemistry.

[141]  Christer S. Ejsing,et al.  Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. , 2006, Analytical chemistry.

[142]  Steven P Gygi,et al.  Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations , 2005, Nature Methods.

[143]  M. Wenk The emerging field of lipidomics , 2005, Nature Reviews Drug Discovery.

[144]  A. Makarov,et al.  The Orbitrap: a new mass spectrometer. , 2005, Journal of mass spectrometry : JMS.

[145]  A. Oberg,et al.  Statistical evaluation of internal and external mass calibration laws utilized in fourier transform ion cyclotron resonance mass spectrometry. , 2005, Analytical chemistry.

[146]  Somnath Datta,et al.  Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens , 2004, Bioinform..

[147]  Weida Tong,et al.  Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence , 2004, Environmental health perspectives.

[148]  A. Helenius,et al.  Roles of N-linked glycans in the endoplasmic reticulum. , 2004, Annual review of biochemistry.

[149]  R. Aebersold,et al.  Proteomics: the first decade and beyond , 2003, Nature Genetics.

[150]  William Stafford Noble,et al.  A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores. , 2003, Journal of proteome research.

[151]  A. Makarov,et al.  Interfacing the orbitrap mass analyzer to an electrospray ion source. , 2003, Analytical chemistry.

[152]  A. Helenius,et al.  Intracellular functions of N-linked glycans. , 2001, Science.

[153]  A. J. Parodi,et al.  Role of N-oligosaccharide endoplasmic reticulum processing reactions in glycoprotein folding and degradation. , 2000, The Biochemical journal.

[154]  B. Chait,et al.  ProFound: an expert system for protein identification using mass spectrometric peptide mapping information. , 2000, Analytical chemistry.

[155]  Makarov,et al.  Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis , 2000, Analytical chemistry.

[156]  A Bairoch,et al.  High-throughput mass spectrometric discovery of protein post-translational modifications. , 1999, Journal of molecular biology.

[157]  F. McLafferty,et al.  Electrospray mass spectra from protein electroeluted from sodium dodecylsulfate polyacrylamide gel electrophoresis gels , 1999, Journal of the American Society for Mass Spectrometry.

[158]  T. Veenstra Electrospray ionization mass spectrometry: a promising new technique in the study of protein/DNA noncovalent complexes. , 1999, Biochemical and biophysical research communications.

[159]  C. Masselon,et al.  Matrix-assisted laser desorption/ionization mass spectrometry of noncovalent protein-transition metal ion complexes. , 1998, Journal of mass spectrometry : JMS.

[160]  G. Hurst,et al.  MALDI-TOF analysis of polymerase chain reaction products from methanotrophic bacteria. , 1998, Analytical chemistry.

[161]  Hermann Kopetz,et al.  Real-time systems , 2018, CSC '73.

[162]  Iwan W. Griffiths,et al.  J. J. Thomson — the Centenary of His Discovery of the Electron and of His Invention of Mass Spectrometry , 1997 .

[163]  R. Dougherty Mass spectrometry principles and applications , 1997 .

[164]  D. Hochstrasser,et al.  Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. , 1996, Biotechnology & genetic engineering reviews.

[165]  Alain Truchaud,et al.  Use of artificial intelligence in analytical systems for the clinical laboratory , 1995, The Journal of automatic chemistry.

[166]  Kai Simons,et al.  The role of n-glycans in the secretory pathway , 1995, Cell.

[167]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[168]  J. Place,et al.  Use of artificial intelligence in analytical systems for the clinical laboratory. , 1994, Annales de biologie clinique.

[169]  A C Ward,et al.  Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra. , 1993, FEMS microbiology letters.

[170]  T. Yip,et al.  New desorption strategies for the mass spectrometric analysis of macromolecules , 1993 .

[171]  A C Ward,et al.  Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. , 1993, FEMS microbiology letters.

[172]  J R Griffiths,et al.  An Investigation of Tumor 1H Nuclear Magnetic Resonance Spectra by the Application of Chemometric Techniques , 1992, Magnetic resonance in medicine.

[173]  Richard D. Smith,et al.  Principles and practice of electrospray ionization—mass spectrometry for large polypeptides and proteins , 1991 .

[174]  Michael Karas,et al.  Matrix‐assisted laser desorption ionization mass spectrometry , 1991 .

[175]  Michael Karas,et al.  Analysis of neutral oligosaccharides by matrix-assisted laser desorption ionization mass spectrometry , 1991 .

[176]  Donald R. Scott,et al.  EXPERT SYSTEM FOR ESTIMATES OF MOLECULAR WEIGHTS OF VOLATILE ORGANIC COMPOUNDS FROM LOW-RESOLUTION MASS SPECTRA , 1991 .

[177]  W. Paul Electromagnetic traps for charged and neutral particles , 1990 .

[178]  Iain D. Craig,et al.  The Handbook Of Artificial Intelligence, Volume One, edited by A. Barr and E.A. Feigenbaum Addison-Wesley, Reading, Massachusetts, 1986, 409 pp., Subject & Name Indices (£23.95) , 1989, Robotica.

[179]  M. Karas,et al.  Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. , 1988, Analytical chemistry.

[180]  Koichi Tanaka,et al.  Protein and polymer analyses up to m/z 100 000 by laser ionization time-of-flight mass spectrometry , 1988 .

[181]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[182]  Carla M. Wong,et al.  Application of knowledge based systems technology to triple quadrupole mass spectrometry (TQMS) , 1986, AAAI 1986.

[183]  John C. Kunz,et al.  Application of Artificial Intelligence to Triple Quadrupole Mass Spectrometry (TQMS) , 1984, IEEE Transactions on Nuclear Science.

[184]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[185]  J. D. Morrison,et al.  High efficiency collision-induced dissociation in an rf-only quadrupole. Interim technical report. [5 to 10 eV] , 1978 .

[186]  R. Cooks,et al.  Direct Analysis of Mixtures by Mass Spectrometry , 1978 .

[187]  A. Marshall,et al.  Fourier Transform Ion Cyclotron Resonance Spectroscopy , 1974 .

[188]  G. Hevesy Francis William Aston, 1877-1945 , 1948, Obituary Notices of Fellows of the Royal Society.

[189]  J. J. Thomson,et al.  LXX.On the number of corpuscles in an atom , 1906 .