TY - JOUR A1 - Prasse, Paul A1 - Iversen, Pascal A1 - Lienhard, Matthias A1 - Thedinga, Kristina A1 - Herwig, Ralf A1 - Scheffer, Tobias T1 - Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction JF - MDPI N2 - Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases. KW - deep neural networks KW - drug-sensitivity prediction KW - anti-cancer drugs Y1 - 2022 U6 - https://doi.org/10.3390/cancers14163950 SN - 2072-6694 VL - 14 SP - 1 EP - 14 PB - MDPI CY - Basel, Schweiz ET - 16 ER - TY - JOUR A1 - Prasse, Paul A1 - Iversen, Pascal A1 - Lienhard, Matthias A1 - Thedinga, Kristina A1 - Bauer, Christopher A1 - Herwig, Ralf A1 - Scheffer, Tobias T1 - Matching anticancer compounds and tumor cell lines by neural networks with ranking loss JF - NAR: genomics and bioinformatics N2 - Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data. Y1 - 2022 U6 - https://doi.org/10.1093/nargab/lqab128 SN - 2631-9268 VL - 4 IS - 1 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Bauer, Chris A1 - Herwig, Ralf A1 - Lienhard, Matthias A1 - Prasse, Paul A1 - Scheffer, Tobias A1 - Schuchhardt, Johannes T1 - Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types JF - Journal of translational medicine N2 - Background: There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods: In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results: We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: . Conclusions: Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs. KW - Literature mining KW - Anti-cancer drugs KW - Tumor types KW - Word embeddings KW - Database Y1 - 2021 U6 - https://doi.org/10.1186/s12967-021-02941-z SN - 1479-5876 VL - 19 IS - 1 PB - BioMed Central CY - London ER - TY - JOUR A1 - Prasse, Paul A1 - Knaebel, Rene A1 - Machlica, Lukas A1 - Pevny, Tomas A1 - Scheffer, Tobias T1 - Joint detection of malicious domains and infected clients JF - Machine learning N2 - Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains. KW - Machine learning KW - Neural networks KW - Computer security KW - Traffic data KW - Https traffic Y1 - 2019 U6 - https://doi.org/10.1007/s10994-019-05789-z SN - 0885-6125 SN - 1573-0565 VL - 108 IS - 8-9 SP - 1353 EP - 1368 PB - Springer CY - Dordrecht ER -