Bioinformatics Data Mining

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.

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

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.

What Data Mining Services We Offer

At CD Genomics, we specialize in curating and mining datasets from a broad range of publicly available sources and databases. Our services are designed to assist you in identifying the most relevant data sources and datasets for your research needs. Whether it's for data landscaping or deeper bioinformatics analyses, our team is equipped to provide comprehensive and tailored support.

For data landscaping, we automatically retrieve metadata for each dataset, such as omics type, associated publications, sample numbers, and any other relevant metadata available from public databases. A dedicated bioinformatician will refine your queries and assess the results to ensure we deliver the most pertinent datasets. We also offer re-analysis of selected datasets using advanced data mining techniques, applying standard analyses or developing bespoke solutions as required.

CD Genomics provides a diverse range of data mining services, tailored to support various research needs, including but not limited to:

Microbiome Data Mining Service: Gain insights into microbial communities by analyzing microbiome datasets, aiding in the understanding of complex interactions within various environments or host systems.

Microarray Expression Data Mining Service: Delve into gene expression data to identify key patterns and relationships across different biological conditions, supporting discoveries in areas like disease progression and therapeutic response.

GWAS Data Mining Service: Utilize Genome-Wide Association Studies to pinpoint genetic variants associated with diseases and traits, enabling a deeper understanding of genetic influences on health and disease.

Clinical Data Mining Service: Extract actionable insights from clinical datasets to improve research outcomes and support evidence-based decision-making in medical practice.

Medical Data Mining Service: Harness medical data to drive discoveries that can enhance patient care and impact healthcare outcomes, including identifying risk factors and optimizing treatment strategies.

Omics Data Mining Service: Focus on single-omics data to uncover specific biological insights in genomics, proteomics, or metabolomics, enabling targeted research on individual components of complex biological systems.

Multi-omics Data Mining Service: Integrate multiple omics datasets to provide a comprehensive view of biological processes, offering a more holistic approach to understanding disease mechanisms and identifying novel therapeutic targets.

Cancer Data Mining Service: Specialize in mining cancer-specific datasets to discover potential biomarkers, therapeutic targets, and insights into cancer biology, supporting advancements in precision oncology.

Single Database Data Mining Services

Our expertise includes mining specific public databases to extract the most relevant data for your research:

Integrated Database Mining Services

For more complex research needs, we offer integrated data mining services that combine datasets from multiple databases, enhancing the depth and breadth of insights:

Relevant Public Databases

The relevant public databases utilized in our data mining services include, but are not limited to:

• Metabolomics Databases: For exploring small molecule profiles and metabolic pathways.
• Biomedical Databases: To access a broad range of clinical and experimental data across various diseases.
• Nucleic Acid Databases: For insights into DNA and RNA sequences and their functions.
• Model Organism Databases: To study genetic and phenotypic data from model organisms.
• Phenotype Databases: For correlating genetic information with phenotypic traits.
• Protein Databases: To analyze protein sequences, structures, and functions.
• Interaction Databases: For understanding protein-protein, protein-DNA, and other molecular interactions.
• Functional Databases: To investigate gene functions, pathways, and networks.
• Metagenomic Databases: For insights into complex microbial communities and their functions.

Our Experience

CD Genomics has a proven track record in providing integrated database mining services that leverage data from multiple sources to generate comprehensive insights. Our team has successfully completed numerous projects, drawing on our extensive experience with various public databases to address complex biological questions.

Our capabilities include:

Integrated Omics Analysis: We excel in projects that involve integrating data from multiple sources like TCGA, GEO, ICGC, and UCSC Xena. This approach allows us to explore complex biological phenomena such as cancer heterogeneity, biomarker discovery, and therapeutic target identification. By combining genomic, transcriptomic, and clinical data, we offer a holistic view of disease mechanisms.

Multi-Database Mining: Our team has extensive experience in mining integrated datasets from combinations like GEO and UCSC Xena, GEO and WES, and analyses involving ArrayExpress, Oncomine, and cBioPortal. This method uncovers unique patterns and associations that are often missed when datasets are analyzed individually.

Cross-Platform Data Integration: We specialize in merging data from diverse sources, such as TCGA and IMvigor210, to provide insights into complex conditions like cancer and immunotherapy responses. Our integrated approach helps identify novel biomarkers and gene signatures that predict therapeutic outcomes, enhancing personalized medicine efforts.

Custom Data Mining Solutions: Our projects frequently involve customized data mining from multiple public databases, including GEO, TCGA, and UCSC Xena. Whether it's identifying high-expression cancer targets or exploring the interplay between genetic mutations and clinical phenotypes, we tailor our analyses to meet your specific research objectives.

Advanced Multi-Omics Integration: By integrating data across various omics layers, such as genomics, proteomics, and metabolomics, we have supported studies in oncology, cardiovascular diseases, and neurological disorders. Our advanced data mining techniques reveal interconnected pathways and molecular networks, driving deeper insights into disease mechanisms.

Database-Specific Expertise: We are adept at mining data from specialized databases, including ArrayExpress, cBioPortal, ICGC, NHANES, Oncomine, SEER, and others. This enables us to deliver focused insights tailored to distinct research areas, such as cancer progression, population health studies, and biomarker validation.

Collaborative Data Integration: We have participated in collaborative projects that require the integration of datasets from diverse sources. For example, combining data from GEO, Oncomine, and cBioPortal has enabled us to identify overlapping features and validate findings across independent studies, enhancing the robustness and reproducibility of our results.

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.