Applying a Big Data Approach to Biomarker Discovery

Lemos, James A. de; Rohatgi, Anand; Ayers, Colby

doi:10.1161/circulationaha.115.019648

Cited by 12 publications

(10 citation statements)

References 11 publications

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“…From a scientific standpoint biomarker discovery should be ideally performed in relatively homogenous populations using narrowly defined endpoints that represent the most specific phenotypes possible. In contrast validation should be performed in diverse cohorts that better reflect the clinical circumstances in which biomarkers might be used [30].…”

Section: Study Design and The Biomarker Discovery Pipeline: Considmentioning

confidence: 99%

“…The reason for this lack of follow up could be because validation studies do not actually take place or because validation studies are carried out and produce negative results, which are not reported (or published). A recent systematic review examined biomarker discovery publications where metabolomics biomarkers had been validated on an external test set (usually by the same group, on the same publication) and showed that apparently equivalent studies, on the same disease, obtain different biomarker lists [30].…”

Section: The Data Analysis Step: Considerations For Complex Diseasementioning

confidence: 99%

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The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Considine

2019

Metabolites

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Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

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Section: Study Design and The Biomarker Discovery Pipeline: Considmentioning

confidence: 99%

Section: The Data Analysis Step: Considerations For Complex Diseasementioning

confidence: 99%

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Considine

2019

Metabolites

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“…The investigators and clinicians in the cardiovascular specialty have extensive experience with discovery science 18 , a robust array of RCT data 109 , a large number of longitudinal cohort studies, and are familiar with mobile technology and implanted medical devices that provide readouts of cardiovascular physiological data, often in real time 110,111 . In addition, cardiovascular investigators are accustomed to using biomarker assays for evaluating patients 112 , incorporating them to enhance the efficiency of clinical trials 67 , and are in a position to use such tests to define which individuals should receive molecularly targeted therapies in the future 113,114 . Basic and clinical cardiovascular investigators have published standards for merging electronic health record data and genomics for research purposes 115 .…”

Section: Precision Participant Descriptormentioning

confidence: 99%

Precision medicine in cardiology

2016

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The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.

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“…Currently, no gold standard exists for what constitutes a good candidate (87). Decision justifications in published reports range from formal statistical inferences of individual proteins (88), panels of proteins after feature selection (89), to additional criteria, such as cutoffs in the raw magnitude of fold-change (e.g., >5-fold changes) in at least n number of patients (90), to more custom modeling that adjust for covariates in risk assessments (31,35).…”

Section: Interpretation and Validation Of Proteomics Datamentioning

confidence: 99%

Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

Lam

Ping

Murphy

2016

Journal of the American College of Cardiology

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Proteomics is a systems physiology discipline to address the large-scale characterization of protein species within a biological system, be it a cell, a tissue, a body biofluid, an organism, or a cohort population. Building on advances from chemical analytical platforms (e.g., mass spectrometry and other technologies), proteomics approaches have contributed powerful applications in cardiovascular biomedicine, most notably in: 1) the discovery of circulating protein biomarkers of heart diseases from plasma samples; and 2) the identification of disease mechanisms and potential therapeutic targets in cardiovascular tissues, in both preclinical models and translational studies. Contemporary proteomics investigations offer powerful means to simultaneously examine tens of thousands of proteins in various samples, and understand their molecular phenotypes in health and disease. This concise review introduces study design considerations, example applications and use cases, as well as interpretation and analysis of proteomics data in cardiovascular biomedicine.

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Applying a Big Data Approach to Biomarker Discovery

Cited by 12 publications

References 11 publications

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Precision medicine in cardiology

Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

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