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Data analysis & consulting services

Statistical, bioinformatics and machine learning consulting

Proteins are biologically very important as they are the executive biomolecules in cells, tissues and organs. In this context, proteomics involves the parallel identification, quantification, and spatial resolution of many proteins in complex samples. Proteomics experiments with a focus on clinical research are indispensable for a comprehensive understanding of biological functions and diseases of high socio-economic relevance for aging societies, such as neurodegenerative diseases, metabolic diseases and cancer.

The Core Facility for Bioinformatics at the Faculty of Medicine "CUBiMed.RUB" at the Faculty of Medicine offers a wide range of tools, services and trainings in the context of bioinformatics for proteomics.

The staff of the research area "Medical Bioinformatics" of the MPC has extensive expertise and experience in the development and application of proteomics tools and resources, statistical analysis, and the application of machine learning methods. Here, the focus is primarily on clinical and human proteomics.

Consulting and analysis services are offered free-of-charge (for scientific users) within the portfolio of the scientific infrastructure facility CUBiMed.RUB to support users in bioinformatics and statistical analysis of proteomics data and associated clinical data. The services are also offered through the German Network for Bioinformatics Infrastructure (de.NBI / ELIXIR-DE), which is funded by the Federal Ministry of Research, Technology and Space (BMFTR, W-de.NBI-005).

Further information and FAQs can be found on the CUBiMed.RUB website: https://www.cubimed.ruhr-uni-bochum.de/faqs/index.html.en

If you have an analysis or consulting request, please contact us directly via cubimed@rub.de.

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Bioinformatics consulting and analysis of proteomics data

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We offer a bioinformatics consulting service regarding the application of our own as well as third-party proteomics software. We advise on data analysis and the selection of suitable software tools. In addition, we develop user-friendly workflows for frequently required analyses and make them available. In addition to the expert advice provided by our bioinformaticians, we also offer to perform the corresponding analyses.


Our services include:

  • Data preprocessing (e.g. conversion, quality control)
  • Peptide identification
  • Protein inference
  • Protein quantification
  • Joint authoring of or support for publications

Contact

cubimed@rub.de

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Statistical consulting and analysis of proteomics data

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We provide support in the planning of proteomics studies, in the selection of suitable analysis methods and in the interpretation and presentation of the results obtained. In addition to the expert advice provided by our biostatisticians, we also offer to perform the corresponding analyses.


Our services include:

  • Data preprocessing (e.g., normalization, treatment of missing values)
  • Quality control (e.g., outlier detection)
  • Support in study and case number planning
  • Planning and analysis of biomarker studies (e.g., proteins and peptides)
  • Application of machine learning methods, such as clustering and classification
  • Classical statistical modeling (e.g., linear models, logistic regression)
  • Visualization of data and results
  • Survival time analysis
  • Support for writing publications
Contact

cubimed@rub.de

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Machine Learning Consulting

We provide support in the planning and development of machine learning projects with proteomics and corresponding clinical data. From discussing what and how many data points are needed to selecting appropriate algorithms to interpreting model outputs and communicating results, we guide you through every step. In addition to consulting, we also offer to perform machine learning analyses.


Our services include:

  • Data assessment (e.g., anomaly detection, exploratory data analysis)
  • Development of (predictive) models (e.g. classification, clustering)
  • Prevention and detection of data leakage throughout all modeling phases
  • Model interpretability and explainability (e.g. SHAP-based explanations)
  • Model validation and performance assessment (e.g., cross-validation, robustness checks)
  • Model performance evaluation and metrics (e.g. Matthews Correlation Coefficient, AUROC)
  • Assistance with technical documentation and publication writing (for the machine learning parts)
Contact

cubimed@rub.de

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