biologIA

Tailor-made software and platforms to automate the analysis of your Biological Data

biologIA  :

You are a Medtech, a Biotech, a Pharmaceutical Industry or a CRO and you wish to:

  • speed up the analysis of biological samples

  • optimize gain of productivity

  • reduce costs of analysis

  • maximize accuracy of results

  • identify biomarkers for diagnostics

 

biologIA is the solution proposed by CYBERnano to develop tailor-made SaaS platforms & dedicated software to automate the analysis of your biological data. biologIA relies on a complete algorithmic toolbox developed by our Data Scientists.

Use Cases ...

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i-Cellulo

A SaaS platform for the automatic statistical analysis of cell impedance signals measured during in vitro tests.

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Angiometrics

Software of image processing for the characterization of anti-vascular effects in oncology.

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nano-XD

Software for the computer-assisted design of nanoparticles activated by

X-ray for applications in radiotherapy.

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EasyBeat

Software for the analysis of electrophysiological and contractility signals of cardiomyocytes (S7B).

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RadioSens

SaaS platform for the analysis of ELISA tests for the prediction of patient radiosensitivity.

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Actimetrix

SaaS platform for the analysis of actimetry signals measured by wearable sensors on patients.

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CardioClinic

Algorithms for the automatic delineation and analysis of ECG signals in ICH-E14 studies.

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i-Viro

Data science to speed up the development of vaccines.

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i-Vivo

SaaS platform for the design of in vivo experiments and the reduction of assays.

Our References in Data Science :

  1. L. Batista, T. Bastogne, A. Delaunois, J.-P. Valentin, and F. Atienzar, “A statistical signal processing method to rank drug effects in cardiomyocyte impedance assays.,” Biomedical Signal Processing and Control, vol. 45, pp. 202–212, 2018.

  2. G. Vogin, T. Bastogne, L. Bodgi, J. Gillet-Daubin, A. Canet, S. Pereira, and N. Foray, “The pATM Immunofluorescence assay: a high-performance radiosensitivity assay to predict post radiotherapy over- reactions.,” International Journal of Radiation Oncology - Biology - Physics, vol. 101, no. 3, pp. 1–8, 2018.

  3. T. Bastogne, J.-L. Marchand, S. Pinel, and P. Vallois, “A branching process model of heterogeneous DNA damages caused by radiotherapy in in vitro cell cultures,” Mathematical Biosciences, 2017.

  4. M. Toussaint, S. Pinel, F. Auger, N. Durieux, M. Thomassin, E. Thomas, A. Moussaron, D. Meng, F. Pl ́enat, M. Amouroux, T. T. Bastogne, C. Frochot, O. Tillement, F. Lux, and M. Barberi-Heyob, “Proton MR Spectroscopy and Diffusion MR Imaging Monitoring to Predict Tumor Response to Inter- stitial Photodynamic Therapy for Glioblastoma,” Theranostics, 2016.

  5. J.-B. Tylcz, K. El Alaoui-Lasmaili, E.-H. Djermoune, N. Thomas, B. Faivre, and T. Bastogne, “Data- driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density,” Biomedical Signal Processing and Control, vol. 20, pp. 52–60, July 2015.

  6. J.-B. Tylcz, T. Bastogne, H. Benachour, D. Bechet, E. Bullinger, H. Garnier, and M. Barberi-Heyob. A Model-based Pharmacokinetics Characterization Method of Engineered Nanoparticles for Pilot Studies. IEEE Transactions on NanoBioscience, pages Volume:PP , Issue: 99, Apr. 2015.

  7. T. Baumuratova, S. Dobre, T. Bastogne, and T. Sauter, “Switch of sensitivity dynamics revealed with DyGloSA toolbox for dynamical global sensitivity analysis as an early warning for system’s critical transition,” PLoS ONE, vol. 8, p. e82973, Dec. 2013.

  8. H. Benachour, T. Bastogne, M. Toussaint, Y. Chemli, A. Sève, C. Frochot, F. Lux, O. Tillement, R. Vanderesse, and M. Barberi-Heyob. Real-time monitoring of photocytotoxicity in nanoparticles- based photodynamic therapy: a model-based approach. PLoS ONE, 7(11):e48617, Nov. 2012.

  9. R. Keinj, T. Bastogne, and P. Vallois, “Tumor growth modeling based on cell and tumor lifespans,” Journal of Theoretical Biology, vol. 312, pp. 76–86, Nov. 2012.

  10. S. Dobre, T. Bastogne, C. Profeta, M. Barberi-Heyob, and A. Richard, “Limits of variance-based sensi- tivity analysis for non- identifiability testing in high dimensional dynamic models,” Automatica, vol. 48, pp. 2740–2749, Aug. 2012.

  11. R. Keinj, T. Bastogne, and P. Vallois, “Multinomial model-based formulations of TCP and NTCP for radiotherapy treatment planning,” Journal of Theoretical Biology, vol. 279, pp. 55–62, June 2011.

  12. T. Bastogne, A. Samson, , P. Vallois, S. Wantz-M ́ezi`eres, S. Pinel, D. Bechet, and M. Barberi-Heyob, “Phenomenological modeling of tumor diameter growth based on a mixed effects model,” Journal of Theoretical Biology, vol. 262, pp. 544–552, 2010.