Genomic Biomarkers
Genomic Biomarkers - Related Videos
08:31
Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
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Cited by 2 •
2013
Single Virus Genomics (SVG) is a method to isolate and amplify the genomes of single virons. Viral suspensions of a mixed assemblage are sorted using flow cytometry onto a microscope slide with discrete wells containing agarose, thereby capturing the virion and reducing genome shearing during downstream processing. Whole genome amplification is achieved using multiple displacement amplification (MDA) resulting in genomic material that is suitable for sequencing.
02:02
Genomics
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2020
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
08:31
Genome Editing
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2023
A well-established technique for modifying specific sequences in the genome is gene targeting by homologous recombination, but this method can be laborious and only works in certain organisms. Recent advances have led to the development of “genome editing”, which works by inducing double-strand breaks in DNA using engineered nuclease enzymes guided to target genomic sites by either proteins or RNAs that recognize specific sequences. When a cell attempts to repair this damage, mutations can be...
08:28
An Overview of bGDGT Biomarker Analysis for Paleoclimatology
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types...
FAQs
Statistical models relate genomic measurements to clinical or experimental outcomes while accounting for variability across observations. In high-dimensional data, many measured features can appear associated with an outcome by chance, so model-based estimation helps quantify which relationships are meaningful. This statistical filtering supports more defensible disease classification, risk prediction, and treatment-response analyses.
An association shows that a genomic measurement relates to an outcome in the analyzed data, but it does not establish that the measurement can classify disease, predict risk, or identify treatment response accurately. Predictive-performance evaluation tests how useful the biomarker is for its intended purpose. This distinction helps prevent statistically noticeable relationships from being treated as dependable decision tools.
Genomic measurements and clinical or experimental outcomes can vary across observations. Statistical models account for that variability when estimating associations, rather than treating every observed difference as a stable signal. Addressing variability makes the analysis more cautious and helps researchers judge whether an apparent relationship is meaningful enough to support prediction or treatment-related interpretation.
Researchers compare genomic measurements with clinical or experimental outcomes, apply statistical models to estimate associations, account for variability, and evaluate predictive performance. The workflow moves from identifying relationships in the data to judging whether those relationships provide useful predictions. Rigorous validation then helps determine whether the resulting biomarker is reliable enough for research or clinical application.
Validation examines whether a biomarker's observed relationship with an outcome is sufficiently dependable for its intended setting. Statistical evaluation considers both the estimated association and predictive performance, rather than relying on a striking pattern alone. This process helps distinguish findings suitable for continued research from biomarkers that may be reliable enough for clinical application.
They can support disease classification, estimation of disease risk, treatment selection, and evaluation of therapeutic outcomes. The appropriate statistical analysis depends on the outcome being studied and on whether the goal is to describe an association or make a prediction. These applications connect genomic measurements with practical questions about disease status and treatment response.
They provide a setting in which statistical reasoning must handle high-dimensional biological data, variable measurements, and clinically meaningful outcomes. Statistical models help estimate associations and separate potentially meaningful signals from random patterns, while predictive-performance assessment evaluates usefulness. This combination makes genomic biomarker research a direct application of statistical modeling, uncertainty management, and outcome-based evaluation.