Bioinformatics Data Mining

Bioinformatics Data Mining Online Inquiry

Bioinformatics-Analysis, a division of CD Genomics, provides customers with professional bioinformatics data mining services, aiming to help you reduce the time and cost of wet laboratory experiments and data generation. With a professional data analysis team, we provide you with customized data mining services to meet your research needs.

Introduction of Data Mining

Data mining (DM) refers to the extraction or "mining" of knowledge from a large amount of data. Data mining is the science of finding new interesting patterns and relationships in large amounts of data. It is defined as "the process of discovering meaningful new associations, patterns and trends by mining a large amount of data stored in a warehouse". Data mining is sometimes called Knowledge Discovery in Database (KDD). It has been successfully applied in bioinformatics, which has abundant data and requires important discoveries such as gene expression, protein modeling, biomarker identification, drug discover and so on. The development of new data mining methods provides a useful way to understand rapidly expanding biological data. Now data mining methods are widely used in bioinformatics data analysis.

Data Mining in Bioinformatics

Bioinformatics is the science of storing, analyzing and utilizing information from biological data (such as genome data, transcriptome data, proteome data, microbial data, metabolome data, microarray chip data, and data generated by wet experiments). Use these data to mine and analyze sequence, molecule, gene expression or pathway information. At the sane, development of novel data mining methods will play a fundamental role in bioinformatics data analysis.

Data from different databases can be mined for single-omics and multi-omics joint analysis. Fig 1. Data from different databases can be mined for single-omics and multi-omics joint analysis. (Momeni Z, et al2020)

Typical Data Mining Pipeline

Process of knowledge discovery through data mining. Fig 2. Process of knowledge discovery through data mining.

Application of Data Mining

With the development of sequencing technology and bioinformatics, more and more biological data and databases are generated, and a large amount of biological data is stored. Therefore, it is becoming more and more important to mine and effectively use existing data through data mining methods.

Biomedical field: Data mining techniques helps to propose proactive research within specific fields of the biomedical industry. And it enables researchers to better understand the biological mechanisms in order to discover new treatments in the fields of medical care and life knowledge.

Animal and plant research: Integrate and analyze data from different species databases to study the evolutionary relationship between different species. Integrate and analyze data from different omics databases of the same species, and conduct a comprehensive and systematic study of the biological mechanism of this species.

CD Genomics relies on years of experience in data mining and data analysis to provide customers with comprehensive data mining and data analysis services. We will search the existing open databases and perform biological information data analysis according to your needs, and finally generate a result report with biological significance. In addition, we also provide sequencing services of different omics to meet your needs. If you have any questions, please feel free to contact us , we will provide you with satisfactory data mining services.

References

  1. Momeni Z, et al. A Survey on Single and Multi Omics Data Mining Methods in Cancer Data Classification[J]. Journal of Biomedical Informatics, 2020, 107:103466.
  2. Khalid R . Application Of Data Mining In Bioinformatics[J]. Indian Journal of Computer Science and Engineering, 2010, 1(2).
  3. Zaki M J ,et al. Data Mining in Bioinformatics (BIOKDD)[J]. Algorithms for Molecular Biology Amb, 2007, 2(1):4-4.
* For Research Use Only. Not for use in diagnostic procedures.
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