The Era of Big Data: From Data-Driven Research to Data-Driven Clinical Care
暂无分享,去创建一个
[1] Victor I. Mikla,et al. 7 – Ultrasound Imaging , 2014 .
[2] Burkhard Morgenstern,et al. Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets , 2014, PloS one.
[3] Bernhard Pfeifer,et al. Bridging Data Management and Knowledge Discovery in the Life Sciences , 2008 .
[4] Eiichiro Fukusaki,et al. Current metabolomics: technological advances. , 2013, Journal of bioscience and bioengineering.
[5] Olivier Bodenreider,et al. Ontologies and Data Integration in Biomedicine: Success Stories and Challenging Issues , 2008, DILS.
[6] M. Cheung,et al. Meta‐analysis in medicine: an introduction , 2010, International journal of rheumatic diseases.
[7] Hui Sun,et al. Mass spectrometry-based metabolomics: applications to biomarker and metabolic pathway research. , 2016, Biomedical chromatography : BMC.
[8] Coral Barbas,et al. Method validation strategies involved in non-targeted metabolomics. , 2014, Journal of chromatography. A.
[9] G. Tseng,et al. Comprehensive literature review and statistical considerations for GWAS meta-analysis , 2012, Nucleic acids research.
[10] Panos M. Pardalos,et al. Data Mining in Biomedicine , 2010 .
[11] Jian Xu,et al. A machine learning framework of functional biomarker discovery for different microbial communities based on metagenomic data , 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).
[12] R. Aebersold,et al. A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery. , 2015, Journal of proteome research.
[13] Bernhard Pfeifer,et al. A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury , 2010, Bioinform..
[14] W. B. Lee,et al. Data Mining in Biomedicine: Current Applications and Further Directions for Research , 2009, J. Softw. Eng. Appl..
[15] Bernhard Pfeifer,et al. A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry , 2008, Bioinform..
[16] Wendy Hall,et al. The Semantic Web Revisited , 2006, IEEE Intelligent Systems.
[17] Alan H. Fielding,et al. Cluster and Classification Techniques for the Biosciences , 2006 .
[18] C. Pasquier. Biological data integration using Semantic Web technologies. , 2008, Biochimie.
[19] C Baumgartner,et al. Marfan Syndrome , 2005, Methods of Information in Medicine.
[20] C. Baumgartner,et al. Diagnostic power of aortic elastic properties in young patients with Marfan syndrome. , 2005, The Journal of thoracic and cardiovascular surgery.
[21] P. Brennan,et al. Proteomics technologies for the global identification and quantification of proteins. , 2010, Advances in protein chemistry and structural biology.
[22] Andrea Calì,et al. Accessing Data Integration Systems through Conceptual Schemas , 2001, ER.
[23] A. Mobasheri,et al. Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. , 2013, Omics : a journal of integrative biology.
[24] Nikolas Mitrou,et al. Bringing relational databases into the Semantic Web: A survey , 2012, Semantic Web.
[25] Frank Baas,et al. Molecular classification of amyotrophic lateral sclerosis by unsupervised clustering of gene expression in motor cortex , 2015, Neurobiology of Disease.
[26] Costel C. Darie,et al. Mass spectrometry for proteomics-based investigation. , 2014, Advances in experimental medicine and biology.
[27] Christian Baumgartner,et al. A bioinformatics framework for genotype-phenotype correlation in humans with Marfan syndrome caused by FBN1 gene mutations , 2006, J. Biomed. Informatics.
[28] C. Baumgartner,et al. Non-invasive diagnosis of liver diseases by breath analysis using an optimized ion–molecule reaction-mass spectrometry approach: a pilot study , 2010, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.
[29] Christian Baumgartner,et al. Bioinformatic-driven search for metabolic biomarkers in disease , 2011, Journal of Clinical Bioinformatics.
[30] Matthias Baldauf,et al. Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages , 2014, BMC Bioinformatics.
[31] L. M. Akella,et al. SeMoP: a new computational strategy for the unrestricted search for modified peptides using LC-MS/MS data. , 2008, Journal of proteome research.
[32] Amarnath Gupta,et al. Mediator infrastructure for information integration and semantic data integration environment for biomedical research. , 2009, Methods in molecular biology.
[33] Subbarao Kambhampati,et al. Integration of biological sources: current systems and challenges ahead , 2004, SGMD.
[34] Ralf Hofestädt,et al. BioDWH: A Data Warehouse Kit for Life Science Data Integration , 2008, J. Integr. Bioinform..
[35] Bambang Parmanto,et al. A framework for designing a healthcare outcome data warehouse. , 2005, Perspectives in health information management.
[36] Xiangdong Wang,et al. Clinical bioinformatics: a new emerging science , 2011, Journal of Clinical Bioinformatics.
[37] E. Worthey. Analysis and Annotation of Whole‐Genome or Whole‐Exome Sequencing Derived Variants for Clinical Diagnosis , 2017, Current protocols in human genetics.
[38] Christian Baumgartner,et al. Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury. , 2008, The Journal of clinical investigation.
[39] Benno Schwikowski,et al. MUDE: a new approach for optimizing sensitivity in the target-decoy search strategy for large-scale peptide/protein identification. , 2010, Journal of proteome research.
[40] Gos Micklem,et al. metabolicMine: an integrated genomics, genetics and proteomics data warehouse for common metabolic disease research , 2013, Database J. Biol. Databases Curation.
[41] Taneth Ruangrajitpakorn,et al. Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework , 2013, BioMed research international.
[42] Igor Jurisica,et al. Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges , 2014 .
[43] Rui Xu,et al. Clustering Algorithms in Biomedical Research: A Review , 2010, IEEE Reviews in Biomedical Engineering.
[44] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[45] Padhraic Smyth,et al. Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.
[46] Michael P Snyder,et al. High-throughput sequencing for biology and medicine , 2013, Molecular systems biology.
[47] B. S. Manjunath,et al. Biological imaging software tools , 2012, Nature Methods.
[48] Adrian Paschke,et al. A journey to Semantic Web query federation in the life sciences , 2009, BMC Bioinformatics.
[49] Marco Viceconti,et al. Computational Biomedicine: Modelling the Human Body , 2014 .
[50] Jian Yang,et al. MitProNet: A Knowledgebase and Analysis Platform of Proteome, Interactome and Diseases for Mammalian Mitochondria , 2014, PloS one.
[51] Henry Pinkard,et al. Advanced methods of microscope control using μManager software. , 2014, Journal of biological methods.
[52] Mark Gerstein,et al. Semantic Web Approach to Database Integration in the Life Sciences , 2007 .
[53] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[54] Christian Baumgartner,et al. Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity , 2015, PLoS Comput. Biol..
[55] Sumeet Dua,et al. Data Mining for Bioinformatics , 2012 .
[56] Guodong Chen,et al. Application of LC/MS to proteomics studies: current status and future prospects. , 2009, Drug discovery today.
[57] Bernhard Pfeifer,et al. Knowledge Discovery in Proteomic Mass Spectrometry Data , 2015 .
[58] Jun Gao,et al. DW4TR: A Data Warehouse for Translational Research , 2011, J. Biomed. Informatics.
[59] Henning Müller,et al. Strategies for health data exchange for secondary, cross-institutional clinical research , 2010, Comput. Methods Programs Biomed..
[60] Bernhard Pfeifer,et al. A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry , 2009, Bioinform..
[61] D. Stekel,et al. A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data , 2015, BMC Genomics.
[62] Christian Baumgartner,et al. Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer. , 2013, Cancer treatment reviews.
[63] Dimitris Kanellopoulos,et al. Data Preprocessing for Supervised Leaning , 2007 .
[64] Andrea Calì,et al. On the Expressive Power of Data Integration Systems , 2002, ER.