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Guide Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine

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Brand New. Seller Inventory X. Seller Inventory Never used!. This presents a huge financial burden for the pharmaceutical companies and the public. Bioinformatics data storage and automated analysis pipelines can also make this knowledge available to future studies. At later stages, side effects or outcomes of the trial can be associated with specific molecular signatures in the patients to understand their mechanisms and design approaches to circumvent them. Indeed, these methods are already in use, and there are already guidelines in place to guide the design of clinical trials using omics data sets [ ].

Thus, as an increased amount of clinical records and associated omics data sets become available to scientists, bioinformatics approaches will play an important role in guiding clinical trials with an increased success rate. In an ideal precision medicine scenario, we would be able to create a widely used and robust clinical tool that can guide doctors with respect to the data required from a patient to provide his subdisease mechanism and guide the choice of therapy and monitoring.

While we are several decades away from such a tool, and indeed from widespread use of any precision medicine approaches at all, it is nevertheless becoming increasingly clear that understanding at the molecular level and creating dynamic mechanistic models of cell functions during disease development and progression are critical for the success of precision medicine.

Precision medicine for all is still a long-term goal for our community. Even not taking into consideration the improvement in global quality of life, studies have demonstrated the cost—benefit of applying such approaches in the clinic [ ]. Additionally, exciting developments in preclinical studies include the use of patient-derived xenograph mouse models of disease e.

We expect current and future advances in proteomics and phosphoproteomics data collection and analysis to greatly improve our understanding of disease development and progression also contributing to improved implementation of precision medicine in real world applications. Precision medicine aims to tailor diagnostic, therapeutic and monitoring approaches to specific patient subgroups. Proteomics and phosphoproteomics data sets can provide mechanistic insight into disease development and are thus valuable for precision medicine approaches. Major challenges presented by these data include the lack of data robustness and standardization as well as the limited proteome and phosphoproteome coverage.

Methods that are developed specifically for these data types as well as their effective integration with other data sets can mitigate the issues.


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Funding for open access publication fees was provided by the European Molecular Biology Laboratory. Girolamo Giudice is a postdoc in the Petsalaki group at the EMBL-EBI since April working on the development of methods for the characterization of context-specific signaling networks. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In.

Advanced Search. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents. Proteomics-derived precision biomarkers and signatures. From lists to integrated networks. Challenges for clinical application. Future directions.

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Proteomics and phosphoproteomics in precision medicine: applications and challenges Girolamo Giudice. Oxford Academic. Google Scholar. Evangelia Petsalaki. Cite Citation. Permissions Icon Permissions. Abstract Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel.

Figure 1. Open in new tab Download slide. Figure 2. Search ADS. Personal omics profiling reveals dynamic molecular and medical phenotypes. The promise of pharmacogenomics in reducing toxicity during acute lymphoblastic leukemia maintenance treatment. The genomic and transcriptomic architecture of 2, breast tumours reveals novel subgroups.

Clinical utility of gene-expression signatures in early stage breast cancer. Landscape of somatic mutations in breast cancer whole-genome sequences. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms: a meta-analysis.


  • Statistical aspect of translational and correlative studies in clinical trials.
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  • Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine.
  • Bioinformatics and Biomarker Discovery: ""Omic"" Data Analysis for Personalized Medicine.
  • Single-Sensor Imaging: Methods and Applications for Digital Cameras!

One third of dynamic protein expression profiles can be predicted by simple rate equations. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. A HUPO test sample study reveals common problems in mass spectrometry—based proteomics.

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives. Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Google Preview. Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Impact of phosphoproteomics in the translation of kinase-targeted therapies. Role of phosphoproteomics in the development of personalized cancer therapies.

Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Proteomics—a blessing or a curse? Application of proteomics technology to transplant medicine. A prostate antigen in sera of prostatic cancer patients cancer research. Biomarker discovery and validation: technologies and integrative approaches.

Measurement of prostate-specific antigen in serum as a screening test for prostate cancer. The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Gene selection for cancer classification using support vector machines—Kernel Machines. System-wide clinical proteomics of breast cancer reveals global remodeling of tissue homeostasis. Phosphoproteomic analysis of cell-based resistance to BRAF inhibitor therapy in melanoma. Single-cell phosphoproteomics resolves adaptive signaling dynamics and informs targeted combination therapy in glioblastoma.

Phosphoproteomic analysis of interacting tumor and endothelial cells identifies regulatory mechanisms of transendothelial migration. Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors. Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Drug resistance mechanisms in colorectal cancer dissected with cell type—specific dynamic logic models. Quantitative proteomic analysis of differentially expressed protein profiles involved in pancreatic ductal adenocarcinoma.

Proteomic analysis to identify biomarker proteins in pancreatic ductal adenocarcinoma. Differential proteomic analysis of human saliva using tandem mass tags quantification for gastric cancer detection. Identification of candidate diagnostic serum biomarkers for Kawasaki disease using proteomic analysis. Searching for biomarkers of heart failure in the mass spectra of blood plasma. Urinary proteomics can define distinct diagnostic inflammatory arthritis subgroups. A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer.

Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis. Artificial neural networks and decision tree model analysis of liver cancer proteomes. Identification of serum biomarkers for colon cancer by proteomic analysis. Early detection of malignant pleural mesothelioma in asbestos-exposed individuals with a noninvasive proteomics-based surveillance tool.

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Diagnosis of multiple cancer types by shrunken centroids of gene expression. Significance analysis of microarrays applied to the ionizing radiation response. Classification algorithms for phenotype prediction in genomics and proteomics. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Associating genes and protein complexes with disease via network propagation.

The power of protein interaction networks for associating genes with diseases. IKAP: a heuristic framework for inference of kinase activities from Phosphoproteomics data. Kinase activity ranking using phosphoproteomics data KARP quantifies the contribution of protein kinases to the regulation of cell viability.

KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis. Knowledge-based analysis for detecting key signaling events from time-series phosphoproteomics data. Benchmarking substrate-based kinase activity inference using phosphoproteomic data.

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Phosphoproteome integration reveals patient-specific networks in prostate cancer. Internet Explorer. Out of stock online. Not available in stores. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research.

Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery. The following ISBNs are associated with this title:. ISBN - X. ISBN - Guest commentary on chapter 4: Integrative approaches to genotype-phenotype association discovery Ana Dopazo. Guest commentary on chapter 5: Advances in biomarker discovery with gene expression data Haiying Wang and Huiru Zheng.

Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine

Guest commentary on chapter 6: Data integration in proteomics and metabolomics for biomarker discovery Kenneth Bryan. Guest commentary on chapter 8: Data integration: The next big hope? Yves Moreau.