Výzkum a projektyVýzkumné skupiny IBA MU

Výzkumné skupiny IBA MU Integrační bioinformatika

Vedoucí výzkumné skupiny Vedoucí výzkumné skupiny: Ing. Vlad Popovici, M.Sc., Ph.D.

The group of Integrative Bioinformatics aims primarily at developing methods for multi-modality and multi-resolution data analysis in life sciences. The vision is to exploit (i) redundancies in data for reinforcing the discoveries and (ii) complementarity for bringing new insights.

Leader of the research group:

Vlad Popovici, Ph.D.

Vlad Popovici, Ph.D.

Contact: popovici@iba.muni.cz

The technological developments allow us to interrogate the same objective reality at different resolutions (e.g. population, organism, tissue, cellular and molecular, etc.) and with different modalities (e.g. imaging, bio-chemical, etc.). Evidently, one would like to be able to account for all these information when mining for new knowledge. The problem is more acute in biomedical context, where the new instruments produce extremely high dimensional streams of data.

In this context and with a special emphasis on oncology data analysis, the Integrative Bioinformatics group works towards creating new methods and implementing the necessary tools in

  • bioinformatics: from data normalization to gene expression signatures
  • digital pathology image analysis: staining normalization, feature extraction and recognition in whole-slide images
  • statistics and machine learning: novel classifiers for high dimensional heterogeneous data.

The Integrative Bioinformatics group has been involved in data analyses of various types cancer, mostly colorectal and breast cancer and has an extensive experience in discovery of novel biomarkers and gene expression signatures.

Bližší informace (v angličtině) najdete na http://bias.cerit-sc.cz/projects.html.

Přehled projektů

  • Integrative development of multimodal risk score for the estimation of relapse in patients with breast carcinoma, funded by the Internal Grant Agency of the Ministry of Health, Czech Republic (IGA NT/14134)
  • MErCuRIC - A phase Ib/II study of MEK1/2 inhibitor PD-0325901 with cMET inhibitor PF-02341066 in KRASMT and KRASWT (with aberrant c-MET) colorectal cancer, Project ID: 602901, Funding: EU FP7
  • HIGEX – Computational framework for joint analysis of histopathology images and gene expression data. Project ID: 4SGA8736. Funded through SoMoPro II programme, by the People Programme (Marie Curie action) of the 7th FP of EU and co-finance by the South-Moravian Region.
  • Predictive signatures in colorectal cancer.
  • Variability of genomic scores in primary vs. metastatic breast carcinomas.

Viz také http://bias.cerit-sc.cz/projects.html

Vybrané publikace

  • Popovici V, Budinska E, Bosman FT, Tejpar S, Roth AD, Delorenzi M. Context-dependent interpretation of the prognostic value of BRAF and KRAS mutations in colorectal cancer. BMC Cancer 2013, 13: 439. doi: 10.1186/1471-2407-13-439.
  • Budinska E, Popovici V, Tejpar S, D'Ario G, Lapique N, Sikora KO, Di Narzo AF, Yan P, Hodgson JG, Weinrich S, Bosman F, Roth A, Delorenzi M. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J Pathol 2013, 231(1): 63–76. doi: 10.1002/path.4212.
  • Popovici V, Budinska E, Tejpar S, Weinrich S, Estrella H, Hodgson G, Van Cutsem E, Xie T, Bosman FT, Roth AD, Delorenzi M. Identification of a poor-prognosis BRAF-mutant-like population of patients with colon cancer. J Clin Oncol 2012, 30(12): 1288–1295. doi: 10.1200/JCO.2011.39.5814.
  • Popovici V, Budinska E, Delorenzi M. Rgtsp: a generalized top scoring pairs package for class prediction. Bioinformatics 2011, 27(12): 1729–1730. doi: 10.1093/bioinformatics/btr233.
  • Popovici V, Chen W, Gallas BG, Hatzis C, Shi W, Samuelson FW, Nikolsky Y, Tsyganova M, Ishkin A, Nikolskaya T, Hess KR, Valero V, Booser D, Delorenzi M, Hortobagyi GN, Shi L, Symmans WF, Pusztai L. Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res 2010, 12(1): R5. doi: 10.1186/bcr2468.
  • MAQC Consortium. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 2010, 28(8): 827–838. doi: 10.1038/nbt.1665.


  • Rgtsp: parallel implementation of top scoring pairs method with extensions
  • WSItk: a toolkit for whole-slide image analysis