Applied bioinformatics and artificial intelligence

Getting the most out of big data for biomedical translation

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The availability of large amounts of data has revolutionized research in the life sciences in the past few years, offering a wide range of opportunities for knowledge gain and future applications. By combining the disciplines of mathematics, computer science, medicine and life sciences, bioinformatics has made it possible to store, categorize, analyze, evaluate and visualize biological data and to simulate biochemical processes. 

Application areas at Fraunhofer ITEM

© Fraunhofer ITEM
Airway recognition in microscopy data by means of neural networks. Based on a microscopic image of an airway (input image, left column), neural networks allow prediction of the airway lumen (right column).

At Fraunhofer ITEM, researchers develop methods and possibilities for the preparation, analysis and visualization of biomedical data, as well as data models and data analysis pipelines. The focus of our research is on the mapping of cellular and regulatory processes and their translation into applications for humans. Bioinformatics methods are used, for example, for personalized tumor therapy to develop optimized testing strategies and for research on RNAs as diagnostic biomarkers and therapeutic targets. For personalized therapies or for patient stratification, the knowledge gained from big data is key to identifying adequate treatment strategies. Stratification also plays a major role for hazard and risk assessment of chemicals, nanomaterials, and environmental exposure, as the sensitivity to noxious agents differs between subpopulations.

In addition, the Fraunhofer researchers are using bioinformatics and artificial intelligence to advance towards intelligent image data analysis and are further developing this technology, so as to optimize the analysis of histological images and support clinical processes. 

Bioinformatics: recent projects and highlights

 

ELISE research project

Based on different machine learning methods, a smart system interprets patient data, recognizes pathological conditions and informs the medical staff in critical situations. 

Threshold of Toxicological Concern

The TTC concept: Some carcinogenic substances do not directly damage DNA, raising the question as to whether such carcinogens should be subject to other regulatory limits.

 

Machine learning

Machine learning makes it easier to analyze tissue slice images, allowing researchers to study the features of asthma and the efficacy of drugs. 

Detecting single cells with the MSK-IMPACT assay

Since tumor cells differ from the primary tumor, it is important to also screen these individual cells for mutations. 

Publications

  • Elrayess, M. A., Cyprian, F. S., Abdallah, A. M., Emara, M. M., Diboun, I., Anwardeen, N., Schuchardt, S., Yassine, H. M. (2022). Metabolic Signatures of Type 2 Diabetes Mellitus and Hypertension in COVID-19 Patients With Different Disease Severity. Frontiers in Medicine 8. doi: 10.3389/fmed.2021.788687 https://www.frontiersin.org/articles/10.3389/fmed.2021.788687/full  - Open Access
  • Escher, S. E., Aguayo-Orozco, A., Benfenati, E., Bitsch, A., Braunbeck, T., Brotzmann, K., Bois, F., van der Burg, B., Castel, J., Exner, T., Gadaleta, D., Gardner, I., Goldmann, D., Hatley, O., Golbamaki, N., Graepel, R., Jennings, P., Limonciel, A., Long, A., Maclennan, R., Mombelli, E., Norinder, U., Jain, S., Capinha, L. S., Taboureau, O. T., Tolosa, L., Vrijenhoek, N. G., van Vugt-Lussenburg, B. M. A., Walker, P., van de Water, B., Wehr, M., White, A., Zdrazil, B., Fisher, C. (2022). Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action. Toxicology in Vitro 79: 105269. doi: 10.1016/j.tiv.2021.105269 https://www.sciencedirect.com/science/article/abs/pii/S0887233321001946  -
  • Hoda, U., Pavlidis, S., Bansal, A. T., Takahashi, K., Hu, S., Ng Kee Kwong, F., Rossios, C., Sun, K., Bhavsar, P., Loza, M., Baribaud, F., Chanez, P., Fowler, S. J., Horvath, I., Montuschi, P., Singer, F., Musial, J., Dahlen, B., Krug, N., Sandstrom, T., Shaw, D. E., Lutter, R., Fleming, L. J., Howarth, P. H., Caruso, M., Sousa, A. R., Corfield, J., Auffray, C., De Meulder, B., Lefaudeux, D., Dahlen, S. E., Djukanovic, R., Sterk, P. J., Guo, Y., Adcock, I. M., Chung, K. F., group, U. B. s. (2022). Clinical and transcriptomic features of persistent exacerbation-prone severe asthma in U-BIOPRED cohort. Clin Transl Med 12(4): e816. doi: 10.1002/ctm2.816 https://onlinelibrary.wiley.com/doi/10.1002/ctm2.816  - Open Access
  • Malik, M. N. H., Waqas, S. F., Zeitvogel, J., Cheng, J., Geffers, R., Gouda, Z. A., Elsaman, A. M., Radwan, A. R., Schefzyk, M., Braubach, P., Auber, B., Olmer, R., Musken, M., Roesner, L. M., Gerold, G., Schuchardt, S., Merkert, S., Martin, U., Meissner, F., Werfel, T., Pessler, F. (2022). Congenital deficiency reveals critical role of ISG15 in skin homeostasis. Journal of Clinical Investigation 132(3). doi: 10.1172/JCI141573 https://www.jci.org/articles/view/141573  - Open Access
  • Metzenmacher, M., Hegedus, B., Forster, J., Schramm, A., Horn, P. A., Klein, C. A., Bielefeld, N., Ploenes, T., Aigner, C., Theegarten, D., Schildhaus, H. U., Siveke, J. T., Schuler, M., Lueong, S. S. (2022). Combined multimodal ctDNA analysis and radiological imaging for tumor surveillance in Non-small cell lung cancer. Translational Oncology 15(1): 101279. doi: 10.1016/j.tranon.2021.101279 https://www.sciencedirect.com/science/article/pii/S1936523321002709?via%3Dihub  - Open Access
  • Saeb, A., Grundmann, S. M., Gessner, D. K., Schuchardt, S., Most, E., Wen, G., Eder, K., Ringseis, R. (2022). Feeding of cuticles from Tenebrio molitor larvae modulates the gut microbiota and attenuates hepatic steatosis in obese Zucker rats. Food & Function [Epub ahead of print]. doi: 10.1039/d1fo03920b https://pubs.rsc.org/en/Content/ArticleLanding/2022/FO/D1FO03920B  
  • Vrijenhoek, N. G., Wehr, M. M., Kunnen, S. J., Wijaya, L. S., Callegaro, G., Mone, M. J., Escher, S. E., Van de Water, B. (2022). Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid. Altex [Epub ahead of print]. doi: 10.14573/altex.2107261 https://www.altex.org/index.php/altex/article/view/2334  - Open Access
  • Wehr, M. M., Sarang, S. S., Rooseboom, M., Boogaard, P. J., Karwath, A., Escher, S. E. (2022). RespiraTox - Development of a QSAR model to predict human respiratory irritants. Regulatory Toxicology and Pharmacology 128: 105089. doi: 10.1016/j.yrtph.2021.105089 https://www.sciencedirect.com/science/article/abs/pii/S0273230021002300?via%3Dihub