BEVITAL AS

Olink® Proteomics

Targeted protein biomarker discovery by Olink® Target 96&48 and Flex panels.

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Online Inquiry

olink@bevital.no

Olink® Proteomics

Targeted protein biomarker discovery by Olink® Target 96&48 and Flex panels.

site logo light 1 e1730194062552

Online Inquiry

olink@bevital.no

Home > Services > Olink

BEVITAL is a service provider of Olink Proteomics and offers Target 96&48 and Flex protein panels using Proximity Extension Assay (PEA) technology. This platform enables our customers to achieve both metabolomics and proteomics data from the same sample aliquots. Olink Proteomics assays require 1μl of sample volume only, and allow the simultaneous analyses of 88 (Target 96) or 40 (Target 48, Flex) samples. Olink´s brochure give you an overview of the various PEA solutions.

OlinkBrochure

Olink brochure

OlinkBrochure

Olink brochure

Olink® Target 96

Olink® Target 96

Target 96 panels include 92 markers each and provide relative quantification related to a particular disease or biological function. 15 different panels are available as described below:

Olink® Target 48

Olink® Target 48

Target 48 panels consist of 45 (43 mouse) cytokines and provide both relative and absolute quantification (pg/ml) from 1μl sample volume.

Combining Target 48 human and Target 48 Immune Surveillance panels enables the detection of biomarkers covering key immunological processes, making it an ideal tool for identifying robust protein biomarker signatures across major therapeutic areas. The combines panels require 2 μl sample volume and deliver absolute quantification of 89 proteins.

Olink®Flex

Olink®Flex

The new Flex panel allows now to mix 5 to 30 markers from a library of about 200 proteins. Each kit has a capacity of 40 samples and provides both relative and absolute quantification (pg/ml) from 1μl sample volume.

Watch our webinar “Introducing next-generation proteomics to your multi-omics studies: novel insights into inflammation and immune activation” and learn more about Olink´s technologies and their application in multi-omics approaches.

Watch video

Olink® Support

Olink® Support

We highly recommend to register for a free account at Olink Insight, which offers comprehensive support for protein and panel selection, data stories, publications, as well as tools for data analysis and interpretation.

For questions regarding Bevital´s Olink service please contact us at olink@bevital.no or try our Olink-trained chatbot.

Please check our Olink FAQ for PEA related questions.

 

Updated September 30, 2025
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Statistical power is the probability that a statistical test will correctly reject a false null hypothesis (H0​) when a specific alternative hypothesis (H1​) is true. H0​ is the null hypothesis, which states there is no effect or no difference. H1​ is the alternative hypothesis, which states there is a real effect or difference. Alpha (α) is the probability of a Type I error (a false positive), which is the risk of incorrectly rejecting the H0​ when it is actually true. You set this value before the experiment, commonly at 0.05. Beta (β) is the probability of a Type II error (a false negative), which is the risk of failing to reject the H0​ when it is actually false.

Power is calculated as 1−β. Increasing power means you are decreasing the probability of making a Type II error.

Several factors can be adjusted to increase the power of a statistical test:

  • Effect Size: This is the magnitude of the difference you are trying to detect. A larger effect size is easier to detect, thus increasing power. 

  • Sample Size: The number of observations in a study. A larger sample size provides more information about the population, reducing the margin of error and increasing the power to detect a true effect.

  • Variation: Refers to the spread or standard deviation of the data within the population. Less variation makes it easier to distinguish a real effect from random noise, thereby increasing power.

  • Alpha (): Increasing the alpha level (e.g., from 0.05 to 0.10) also increases power, but at the cost of a higher risk of a Type I error. This trade-off is often undesirable.

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Vollset, S E; Nygârd, O; Refsum, H; Ueland, P M

Coffee and homocysteine Miscellaneous

2000, ISSN: 0002-9165.

Links | BibTeX

562.

Schneede, J; Refsum, H; Ueland, P M

Biological and environmental determinants of plasma homocysteine Journal Article

In: Semin Thromb Hemost, vol. 26, no. 3, pp. 263–279, 2000, ISSN: 0094-6176.

Abstract | Links | BibTeX

562 entries « ‹ 29 of 29 › »

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