Relevant bibliographies by topics / Biomarker discovery research

Academic literature on the topic 'Biomarker discovery research'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Biomarker discovery research"

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Chen, Jiwen, and Naiyu Zheng. "Accelerating protein biomarker discovery and translation from proteomics research for clinical utility." Bioanalysis 12, no. 20 (October 2020): 1469–81. http://dx.doi.org/10.4155/bio-2020-0198.

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Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.
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McNamara, Aoife E., and Lorraine Brennan. "Potential of food intake biomarkers in nutrition research." Proceedings of the Nutrition Society 79, no. 4 (July 2, 2020): 487–97. http://dx.doi.org/10.1017/s0029665120007053.

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The influence of dietary habits on health/disease is well-established. Accurate dietary assessment is essential to understand metabolic pathways/processes involved in this relationship. In recent years, biomarker discovery has become a major area of interest for improving dietary assessment. Well-established nutrient intake biomarkers exist; however, there is growing interest in identifying and using biomarkers for more accurate and objective measurements of food intake. Metabolomics has emerged as a key tool used for biomarker discovery, employing techniques such as NMR spectroscopy, or MS. To date, a number of putatively identified biomarkers were discovered for foods including meat, cruciferous vegetables and legumes. However, many of the results are associations only and lack the desired validation including dose–response studies. Food intake biomarkers can be employed to classify individuals into consumers/non-consumers of specific foods, or into dietary patterns. Food intake biomarkers can also play a role in correcting self-reported measurement error, thus improving dietary intake estimates. Quantification of food intake was previously performed for citrus (proline betaine), chicken (guanidoacetate) and grape (tartaric acid) intake. However, this area still requires more investigation and expansion to a range of foods. The present review will assess the current literature of identified specific food intake biomarkers, their validation and the variety of biomarker uses. Addressing the utility of biomarkers and highlighting gaps in this area is important to advance the field in the context of nutrition research.
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Cai, Yanning, and Qian Dong. "Metabonomics research accelerates discovery of medical biomarkers." E3S Web of Conferences 245 (2021): 03048. http://dx.doi.org/10.1051/e3sconf/202124503048.

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Biomarker refers to a characteristic that can be objectively detected and evaluated, and can be used as an indicator of normal biological process, pathological process or therapeutic intervention pharmacological response. As one of the key words of individualized medicine, the search and discovery of valuable biomarkers has become a research hotspot in the current medical field.
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Zhao, Xuemei, Vijay Modur, Leonidas N. Carayannopoulos, and Omar F. Laterza. "Biomarkers in Pharmaceutical Research." Clinical Chemistry 61, no. 11 (November 1, 2015): 1343–53. http://dx.doi.org/10.1373/clinchem.2014.231712.

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Abstract BACKGROUND Biomarkers are important tools in drug development and are used throughout pharmaceutical research. CONTENT This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development—acknowledging that many pharmaceutical development biomarkers are not published—we performed a focused PubMed search employing “biomarker” and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease. SUMMARY Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.
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Dean, Brian. "Dissecting the Syndrome of Schizophrenia: Progress toward Clinically Useful Biomarkers." Schizophrenia Research and Treatment 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/614730.

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The search for clinically useful biomarkers has been one of the holy grails of schizophrenia research. This paper will outline the evolving notion of biomarkers and then outline outcomes from a variety of biomarkers discovery strategies. In particular, the impact of high-throughput screening technologies on biomarker discovery will be highlighted and how new or improved technologies may allow the discovery of either diagnostic biomarkers for schizophrenia or biomarkers that will be useful in determining appropriate treatments for people with the disorder. History tells those involved in biomarker research that the discovery and validation of useful biomarkers is a long process and current progress must always be viewed in that light. However, the approval of the first biomarker screen with some value in predicting responsiveness to antipsychotic drugs suggests that biomarkers can be identified and that these biomarkers that will be useful in diagnosing and treating people with schizophrenia.
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Li, Rebecca, and Ida Sim. "How Clinical Trial Data Sharing Platforms Can Advance the Study of Biomarkers." Journal of Law, Medicine & Ethics 47, no. 3 (2019): 369–73. http://dx.doi.org/10.1177/1073110519876165.

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Although data sharing platforms host diverse data types the features of these platforms are well-suited to facilitating biomarker research. Given the current state of biomarker discovery, an innovative paradigm to accelerate biomarker discovery is to utilize platforms such as Vivli to leverage researchers' abilities to integrate certain classes of biomarkers.
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Zhang, Aihua, Hui Sun, Guangli Yan, Ping Wang, and Xijun Wang. "Metabolomics for Biomarker Discovery: Moving to the Clinic." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/354671.

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To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases.
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Njoku, Kelechi, Davide Chiasserini, Anthony D. Whetton, and Emma J. Crosbie. "Proteomic Biomarkers for the Detection of Endometrial Cancer." Cancers 11, no. 10 (October 16, 2019): 1572. http://dx.doi.org/10.3390/cancers11101572.

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Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.
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Liu, Qiang, Jianxin Guo, Jinghong Cui, Jing Wang, and Ping Yi. "Discover the Molecular Biomarker Associated with Cell Death and Extracellular Matrix Module in Ovarian Cancer." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/735689.

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High throughput technologies have provided many new research methods for ovarian cancer investigation. In tradition, in order to find the underlying functional mechanisms of the survival-associated genes, gene sets enrichment analysis (GSEA) is always regarded as the important choice. However, GSEA produces too many candidate genes and cannot discover the signaling transduction cascades. In this work, we have used a network-based strategy to optimize the discovery of biomarkers using multifactorial data, including patient expression, clinical survival, and protein-protein interaction (PPI) data. The biomarkers discovered by this strategy belong to the network-based biomarker, which is apt to reveal the underlying functional mechanisms of the biomarker. In this work, over 400 expression arrays in ovarian cancer have been analyzed: the results showed that cell death and extracellular module are the main themes related to ovarian cancer progression.
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Majkić-Singh, Nada. "What is a Biomarker? From its Discovery to Clinical Application." Journal of Medical Biochemistry 30, no. 3 (July 1, 2011): 186–92. http://dx.doi.org/10.2478/v10011-011-0029-z.

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What is a Biomarker? From its Discovery to Clinical ApplicationThe term biomarker in medicine most often stands for a protein measured in the circulation (blood) whose concentration indicates a normal or a pathological response of the organism, as well as a pharmacological response to the applied therapy. From a wider perspective, a biomarker is any indicator that is used as an index of the intensity of a disease or other physiological state in the organism. This means that biomarkers have a very important role in medical research and practice providing insight into the mechanism and course of a disease. Since a large number of biomarkers exist today that are used for different purposes, they have been classified into: 1) antecedent biomarkers, indicating risk of disease occurrence, 2) screening biomarkers, used to determine a subclinical form of disease, 3) diagnostic biomarkers, revealing an existing disease, 4) staging biomarkers, that define the stage and severity of a disease, and 5) prognostic biomarkers, that confirm the course of disease, including treatment response. Regardless of their role, their clinical significance depends on their sensitivity, specificity, predictive value, and also precision, reliability, reproducibility, and the possibility of easy and wide application. For a biomarker to become successful, it must undergo the process of validation, depending on the level of use. It is very important for every suggested biomarker, according to its purpose or its nature, to possess certain characteristics and to meet the strict requirements related to sensitivity, accuracy and precision, in order for the proper outcome to be produced in the estimation of the state for which it is intended. Finally, the development of guidelines for biomarker application is very important, based on well defined and properly conducted assessments of biomarker determination, providing the means by which research is translated into practice and allowing evidence based on facts to promote the clinical application of new biomarkers.
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Dissertations / Theses on the topic "Biomarker discovery research"

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Grigsby, Claude Curtis. "A Comprehensive Tool and Analytical Pathway for Differential Molecular Profiling and Biomarker Discovery." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1387540709.

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Miller, Cecelia R. Miller. "Discovery and Functional Interrogation of Biomarkers Related to Therapeutic Response in Chronic Lymphocytic Leukemia." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468953869.

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Prescott, Jeffrey William. "Computer-assisted discovery and characterization of imaging biomarkers for disease diagnosis and treatment planning." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280194844.

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Kuntala, Prashant Kumar. "Optimizing Biomarkers From an Ensemble Learning Pipeline." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1503592057943043.

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Kemperman, Ramses Franciscus Jacobus. "Nutrition and biomarkers in psychiatry research on micronutrient deficiencies in schizophrenia, the role of the intestine in the hyperserotonemia of autism, and a method for nonhypothesis driven discovery of biomarkers in urine /." [S.l. : Groningen : s.n. ; University Library of Groningen] [Host], 2007. http://irs.ub.rug.nl/ppn/305278908.

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Atkinson, Kelly Rene LeFevre. "Proteomic biomarker discovery for preeclampsia." 2008. http://hdl.handle.net/2292/2565.

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Preeclampsia is a serious multisystem complication of late pregnancy with adverse effects for mothers and babies. Currently this disorder is diagnosed from clinical observations occurring late in the disease process. Unknown factors in the maternal circulation, possibly released by the preeclamptic placenta, have been linked to the pathophysiological changes characteristic of the disorder. The research in this thesis used proteomic techniques to identify putative preeclampsia biomarkers from two sources: secreted from a placental cell line undergoing differentiation, and directly sampled from the serum and plasma of women with late-onset preeclampsia. The first part of this research examined the secreted proteome of a placental choriocarcinoma cell line (BeWo) undergoing forskolin-mediated differentiation. Development of serum-free culture techniques enabled analysis of these secreted proteins by two-dimensional gel electrophoresis (2DE). Statistical testing revealed the significant involvement of seven spots during this differentiation model, with VE-cadherin and matrix metalloproteinase 2 among the proteins identified. In the second part of this research, maternal serum and plasma proteins were compared from women with preeclampsia and healthy pregnant women. Serum samples were analyzed using 2DE, and plasma was subjected to difference gel electrophoresis (DIGE). Bioinformatic analysis of both datasets identified multiple spot clusters able to classify samples according to disease state. Five of these serum proteins were differentially regulated in preeclampsia, including two isoforms of apolipoprotein E whose isoform-specific expression was confirmed using western blots. Analysis of plasma from preeclamptic women identified six proteins, again including apolipoprotein E. Proteins from both studies are linked to preeclampsia pathophysiology through lipid transport, complement, and retinol transport systems. The culture methods and secreted proteomic techniques developed in this work have uncovered proteins in a placental cell line and maternal serum and plasma that are associated with preeclampsia. These methods can be extended to any system where secreted proteins are of interest. The differentially regulated proteins found in this study provide an important first step towards developing effective biomarkers for diagnosing and/or predicting preeclampsia.
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Djukic, Michael. "Proteomic investigations and biomarker discovery in transient ischaemic attack." Thesis, 2017. http://hdl.handle.net/2440/112817.

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Between 15-26% of ischaemic strokes are preceded by transient ischaemic attack (TIA) making accurate and timely diagnosis of TIA important for stroke prevention. However, TIA diagnoses are highly reliant on subjective history gathering and clinical assessments to differentially diagnose true TIA conditions from mimic presentations. Unfortunately, the subjective nature of TIA diagnosis has created a surprisingly high amount of variability between diagnoses made by physicians and specialist neurologists. Use of biomarker tests could offer an objective quantitative measuring tool that reduces inter-observer variation through the establishment of standardised quantitative measures and improved reproducibility. When used in combination with comprehensive clinical assessments and neurological imaging, biomarkers may offer a useful adjunct to assist a treating clinician to accurately and reliably interpret the clinical finding and confidently diagnose and treat a TIA or mimic condition. This thesis proposes a framework for undertaking an exploration of the human plasma proteome, and performs the very first proteomic pilot study that identifies candidate plasma protein biomarkers associated with TIA, which could also be used to distinguish from mimic presentations.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, Adelaide Medical School, 2017.
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Yan, Jingwen. "Mining brain imaging and genetics data via structured sparse learning." 2015. http://hdl.handle.net/1805/8028.

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Indiana University-Purdue University Indianapolis (IUPUI)
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.
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Books on the topic "Biomarker discovery research"

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Feng, Wang, ed. Biomarker methods in drug discovery and development. Totowa, NJ: Humana Press, 2008.

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Haghighi, Afshin Borhani, and Bernadette Kalman. Other Proven and Putative Autoimmune Disorders of the CNS. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0094.

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Behcet’s Disease (BD) is a multiorgan disorder characterized by oral and genital ulceration, uveitis, and dermatological symptoms. BD is most prevalent in the Mediterranean countries and East Asia, but also occurs in Europe and North America. The etiology remains unknown. Evidence suggests that BD is an autoimmune disorder with complex traits. Neuro-Behcet’s Syndome (NBS) develops in about 5% to 30% of patients with BD and presents with parenchymal or nonparenchymal pathology. The course of NBS is highly variable. Treatment strategies include modulations of the immune response and tissue degeneration, along with symptomatic medications. Main directions of current research include genomic studies, biomarker discovery, and inventive drug- development strategies.
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Mondello, Stefania, Ronald L. Hayes, András Büki, Frank C. Tortella, and Kevin K. W. Wang, eds. Towards translating research to clinical practice: Novel Strategies for Discovery and Validation of Biomarkers for Brain Injury. Frontiers SA Media, 2015. http://dx.doi.org/10.3389/978-2-88919-391-2.

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Juan, Hsueh-Fen, and Hsuan-Cheng Huang. Systems Biology: Applications in Cancer-Related Research. World Scientific Publishing Co Pte Ltd, 2012.

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Gipson, Tanjala T., Andrea Poretti, Rebecca McClellan, and Michael V. Johnston. Tuberous Sclerosis Complex. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0050.

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Tuberous sclerosis complex (TSC) is a disease, commonly classified as a neurocutaneous disorder, which may result in benign tumors throughout the brain and body, skin lesions, epilepsy, and cognitive/behavioral difficulties. Scientific discovery in TSC has resulted in the availability of treatments designed to target the neurobiological core of TSC in children. However, research is needed to determine if these treatments are effective for multiple aspects of the TSC phenotype in children. Current pediatric research studies have focused on the effects of early treatment of epilepsy as well as identification of potential biomarkers. This chapter reviews the aspects of TSC unique to pediatric patients, the status of current research, and future directions.
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Book chapters on the topic "Biomarker discovery research"

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Liu, Liu, Lulu Jia, and Xuejiao Liu. "Urimem Facilitates Kidney Disease Biomarker Research." In Urine Proteomics in Kidney Disease Biomarker Discovery, 23–30. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9523-4_3.

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Goyal, Ramesh K., and Geeta Aggarwal. "Biomarker-Based Drug Discovery with Reverse Translational Approach." In Biomedical Translational Research, 123–40. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9232-1_9.

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Desai, Aarti N., and Abhay Jere. "Next-Generation Sequencing for Cancer Biomarker Discovery." In Next Generation Sequencing in Cancer Research, Volume 2, 103–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15811-2_7.

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Puchois, Pascal, Lisa B. Miranda, and Alain van Gool. "CHAPTER 3. Introduction: The Cardinal Role of Biobanks and Human Biospecimen Collections in Biomarker Validation: Issues Impeding Impact of Biomarker Research Outcomes." In Drug Discovery, 73–110. Cambridge: Royal Society of Chemistry, 2013. http://dx.doi.org/10.1039/9781849734363-00073.

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Bremer, Anne, Elisabeth Wik, and Lars A. Akslen. "HER2 Revisited: Reflections on the Future of Cancer Biomarker Research." In Human Perspectives in Health Sciences and Technology, 97–119. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92612-0_7.

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AbstractIn this chapter, we revisit the successful story of the HER2 biomarker for breast cancer, to reflect on the conditions of its inception, some of the reasons for its success, and the challenges met along the way. HER2 is a standard in the field of cancer biomarker research, against which all biomarkers are measured. It is also one of the central arguments used for illustrating the feasibility and desirability of precision oncology. But critically revisiting the story of HER2 shows us that it too faced a winding road from its discovery in the lab to its use in the clinic, and that it currently operates in a context of high levels of biological complexity and persistent uncertainties, in particular with regard to cancer heterogeneity and its implications. By drawing a parallel between the story of HER2 and a ‘scientific bandwagon’, we examine some of the legal, social, and economic challenges and dilemmas that HER2 faces, and conclude with some reflections on the future of cancer biomarker research. Notably, we highlight the need for a greater focus on ‘good enough’ biomarkers, particularly in the setting of precision oncology driven by hyper-precision and the wish for molecular certainty. We emphasise the importance of being open about the low success rate of 1% of published biomarkers which reach clinical practice when justifying the risks and opportunity costs of precision oncology.
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Zou, Lili, and Wei Sun. "Human Urine Proteome: A Powerful Source for Clinical Research." In Urine Proteomics in Kidney Disease Biomarker Discovery, 31–42. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9523-4_4.

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Gibson, David S., David Bramwell, and Caitriona Scaife. "Difference In-Gel Electrophoresis: A High-Resolution Protein Biomarker Research Tool." In Biomarker Methods in Drug Discovery and Development, 189–209. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-463-6_9.

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Lv, Yang, Guangyan Cai, and Xiangmei Chen. "Applications of Urinary Proteomics in Renal Disease Research Using Animal Models." In Urine Proteomics in Kidney Disease Biomarker Discovery, 145–50. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9523-4_14.

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Baars, Erik W., Andreas F. M. Nierop, and Huub F. J. Savelkoul. "Seasonal Allergic Rhinitis and Systems Biology-Oriented Biomarker Discovery." In General Methods in Biomarker Research and their Applications, 1–18. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7740-8_33-1.

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Uehara, Takeki, Yuping Wang, and Weida Tong. "Toxicogenomic and Pharmacogenomic Biomarkers for Drug Discovery and Personalized Medicine." In General Methods in Biomarker Research and their Applications, 1–25. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7740-8_19-1.

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Conference papers on the topic "Biomarker discovery research"

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Srivastava, Sudhir. "Abstract CN05-01: An overview of biomarker discovery and development." In Abstracts: AACR International Conference on Frontiers in Cancer Prevention Research‐‐ Oct 22-25, 2011; Boston, MA. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1940-6207.prev-11-cn05-01.

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Carr, Steven A. "Abstract PL01-03: Quantitative biology and biomarker discovery without immunoassays." In Abstracts: AACR International Conference on Frontiers in Cancer Prevention Research‐‐ Oct 22-25, 2011; Boston, MA. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1940-6207.prev-11-pl01-03.

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Mougeot, Jean-Luc, Nirav Shah, Aneeta Uppal, Braxton Noll, Mohamed Shehab, and Farah Mougeot. "05.07 Biomarker discovery in sjögren’s syndrome: database analysis and web interface design." In 37th European Workshop for Rheumatology Research 2–4 March 2017 Athens, Greece. BMJ Publishing Group Ltd and European League Against Rheumatism, 2017. http://dx.doi.org/10.1136/annrheumdis-2016-211052.7.

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Saccomano, Nicholas A., Sheri Wilcox, Dom Zichi, Stephan Kraemer, Nebojsa Janjic, Larry Gold, Deb Ayers, et al. "Highly multiplexed SOMAmer assays as a flexible platform for protein biomarker discovery research." In AACR International Conference: Molecular Diagnostics in Cancer Therapeutic Development– Sep 27-30, 2010; Denver, CO. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/diag-10-a18.

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Karamboulas, Christina, Jeffrey P. Bruce, Kara M. Ruicci, Wei Xu, Anthony C. Nichols, and Laurie Ailles. "Abstract IA02: Utilizing patient-derived xenografts for prognostication and biomarker discovery." In Abstracts: AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 29-30, 2019; Austin, TX. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1557-3265.aacrahns19-ia02.

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Thompson, Ian M. "Abstract PL04-01: Early Detection Research Network: Biomarker discovery and validation - A team approach." In Abstracts: AACR International Conference on Frontiers in Cancer Prevention Research‐‐ Oct 22-25, 2011; Boston, MA. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1940-6207.prev-11-pl04-01.

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Zajchowski, D. A., M. Whitlow, K. M. Zajchowski, and L. K. Shawver. "Abstract POSTER-TECH-1127: Genomic profiles inform treatment decisions and enable future drug/biomarker discovery." In Abstracts: 10th Biennial Ovarian Cancer Research Symposium; September 8-9, 2014; Seattle, WA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1557-3265.ovcasymp14-poster-tech-1127.

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Ogino, Shuji. "Abstract PL05-04: Tumor biomarker discovery for aspirin chemoprevention by molecular pathological epidemiology (MPE) approach." In Abstracts: Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; Oct 27-30, 2013; National Harbor, MD. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1940-6215.prev-13-pl05-04.

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Leung, Felix, Marcus Q. Bernardini, Blaise Clarke, Marjan Rouzbahman, Eleftherios P. Diamandis, and Vathany Kulasingam. "Abstract B13: Discovery of novel subtype-specific ovarian cancer biomarkers via integrated tissue proteomics." In Abstracts: AACR Special Conference: Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; October 17-20, 2015; Orlando, FL. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1557-3265.ovca15-b13.

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Block, Timothy M. "Abstract CN01-02: Glycoproteomic discovery of liver cancer biomarkers: Be careful how you use it!" In Abstracts: AACR International Conference on Frontiers in Cancer Prevention Research‐‐ Oct 22-25, 2011; Boston, MA. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/1940-6207.prev-11-cn01-02.

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Reports on the topic "Biomarker discovery research"

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Brosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.

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Abstract:
Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.
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