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Bioinformatics Data Mining Service

Bioinformatics Data Mining Service
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目录号
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G02100000
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New!
Data mining using machine learning (AI) for unique epigenetic data insights

WHITE PAPERS

Powerful new insights with epigenetic data mining.
A study to distinguish smokers from non-smokers using just one droplet of blood

Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.
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Data mining on DNA methylation data in cancer samples
Distinguishing normal from breast cancer tissue

Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.

One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.
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Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.

If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially “finding a needle in a haystack.”

Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.

Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:

Epigenetic data Transcriptomic data

DNA Methylation (RRBS, WGBS, EPIC arrays)

ChIP-sequencing

ATAC-seq

mRNA-sequencing

Small and long non coding RNA

Single-cell RNA-sequencing

Biological Interpretation

Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.

  • Data mining modules
    1. Feasibility study
    • Assessment of data characteristics and applicability of different machine learning (ML)
    • Prototypic analyses:
    • Initial feasibility report

    1. Data Mining
    • Machine learning on data
    • Data evaluation and validation
    • Report generation

     

    1. Data Interpretation
    • Integration of background knowledge
    • Functional interpretation / pathway mining
    • Scientific reporting
  • Integration with wet lab services and bioinformatics

    A clear advantage of Diagenode’s data mining services is the close connection with  other service offerings like wet lab analysis services and bioinformatics services. You can retrieve a full service package from a single source.

    Read about our wetlab services

  •  文档
    Epigenomics Profiling Services FLYER
    Chromatin analysis DNA methylation services RNA-seq analysis     		Download
  •  出版物

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