Computational methods for biomarker discovery

  • How biomarkers are detected?

    Detection of protein biomarkers typically rely on established immunoassays such as ELISA, protein microarrays and flow cytometry.
    These techniques measure the amount of biomarker present in a sample through binding of a labeled antigen..

  • What are the approaches for biomarker identification?

    In summary, mechanism-centric approaches (based on gene co-expression networks, regulatory networks, PPI networks, and molecular pathways) identify biomarkers that are biologically meaningful, interpretable, reproducible, have higher translational potential, and provide greater predictive power over biomarkers .

  • What are the methods used in biomarker discovery?

    By screening many possible biomolecules at a time, a parallel approach can be attempted; genomics and proteomics are some technologies used in this process.
    Secretomics has also emerged as an important technology in the high-throughput search for biomarkers; however, significant technical difficulties remain..

  • What are the technologies used in biomarker discovery?

    Metabolomics, proteomics and epigenetics are all strategies that are used to discover biomarkers.
    Metabolomics refers to the study of changing metabolite levels over time in response to a stimulus – for example a drug or disease..

  • What is mass spectrometry based proteomics for biomarker discovery?

    MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring.
    Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit..

  • Abstract.
    Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients.
    Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants.
  • Biomarker discovery relies on the collection of large specimen collections that have been molecularly profiled.
    Specimen libraries must also be associated with the disease characteristics of interest such as disease subtype, prognosis, or drug response versus nonresponse.
  • One of the main challenges in biomarker discovery is the high number of false discoveries.
    This occurs when initial scientific findings associated with a new biomarker cannot be reproduced by other laboratories or independent samples.
  • The most common approach towards biomarker discovery in proteomics involves assessing relative differences in proteins between cases and controls, for example, identifying which protein is differentially expressed between IBD patients and healthy controls.
Artificial intelligence and machine learning (ML) approaches are adept at biomarker discovery and can extract subtle signatures from large intricate datasets. Moreover, besides genomics, interest has grown in leveraging alternate datasets for biomarker discovery, specifically in metabolomics.
Mar 27, 2017We present an overview of computational methods One case study is exemplarily presented, and relevant computational biomarker discovery 
Overall, computational methods, such as ML, are making large strides in biomarker discovery. Coupled with complex datasets, including integrated multimodal datasets, computational methods stand to advance the biomarker field significantly, with the anticipated goal of realizing personalized medicine.
Recently, advances in computational methods have enabled analysis of complex biological datasets. Artificial intelligence and machine learning (ML) approaches are adept at biomarker discovery and can extract subtle signatures from large intricate datasets.

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