Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular Biology¿ series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls.Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

Fully updated overview on machine learning techniques applied to biological problems

Section I: Algorithms for interpreting MS and MS/MS dataIntroduction: Assigning peptides and proteins to MS spectraItziar Frades, Ewa Gubb and Rune MatthiesenIdentification of Post-translational Modifications via Blind Search of Mass-SpectraStephen TannerPeptide sequence tags for fast database search in mass-spectrometryAri FrankDe novo sequencing with PepHMMTing ChenThe Global Proteome Machine OrganizationRon BeavisGutenTag: Software for automated sequence tag identification of peptidesJohn Yates, IIIManual analysis emulatorKatheryn ResingAscore for phosphorylation sitesSteven GygiSILVERSteven GygiAldente: PEPTIDE MASS FINGERPRINTING TOOLRon AppelNovel Peptide Identification using ESTs and Genomic SequencesNathan EdwardsSection II: Quantitative proteomicsOverview of chemical labeling methods for quantitative proteomicsShabaz MohammedQuantitative proteomics using SILACJens S. Andersen/Jakob Bunkenborg/Peter MortensenMSquantJens S. Andersen/Jakob Bunkenborg/Peter MortensenASAPRatioLi X-JXPRESSHan DKQuantitative algorithms in VEMSRune MatthiesenQuantitation based on LC-MS intensity profilesJennifer ListgartenImproving identification of protein complexes by using quantitative informationMarkus MüllerOpenMSKnut ReinertChallenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel ElectrophoresisEllen Mosleth FærgestadQuantitation from 2D gel spotsPeter F. LemkinSectionIII: Finding biomarkers in MS dataIntroduction: Classification by machine learningIñaki Inza inzaFeature selection and machine learning with mass spectrometry dataSusmita DattaIdentification of biomarkers from mass spectrometry data using a 'common' peak approachTadayoshi FushikiAnnotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkersYudi PawitanAnalysis of mass spectral serum profiles for biomarker selectionHabtom W. RessomComparison of statistical methods for classification of ovarian cancer using mass spectrometry dataHongyu ZhaoA novel approach for clustering proteomics data using Bayesian fast Fourier transformHalima BensmailA suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MSMartin McIntoshSemi-supervised LC/MS alignment for differential proteomicsBernd FischerPerspective: A Program to Improve Protein Biomarker Discovery forCancerLeland HartwellSection IV: Data storagePRIDE: open source proteomics identifications databasePhil JonesData storage using CPASTed HolzmanGPMDB: The Global Proteome Machine Organization Proteomics DatabaseRon BeavisData storage in dbVEMSRune MatthiesenPROTEIOS: an open source proteomics initiativeJari HäkkinenDatabase tool for differential peptide expressionMark K TitulaerSection V: System biologyOntologies and databases at EBISandra OrchardTowards understanding biological processes: a text mining approachAlberto