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SUMMARY

A main objective of this deliberate is to analyze the data obtained in tasks 3.3 and 3.4 of WP3, for which a phenotype-molecule library (iONCOwareTM) was generated in order to present all the results obtained from the methods, techniques and tools used in the project. Thanks to this, the customized result can be extracted quickly and concisely and therefore provide the final tool to the stakeholders identified in the zebraONCOfish project.

To evaluate the active and exploratory characteristics of the computational model and its ability to handle multimodality, iONCOwareTM presents a specific AI-based algorithm to provide active, dynamic and accurate behavior.

From raw data from systems to prediction, there is a long way that starts first by automatically extracting the knowledge from the data – that is, the raw data aggregated into understandable facts or events. This implies understanding the content of the data, that is, having a context to perfectly adapt to the algorithms that will be used, in particular the Machine Learning (ML) and Data Mining (DM) algorithms. Therefore, this task was carried out by standardizing the protocol of ten stable cell lines and five biopsies that could be recovered after the dramatic events associated with COVID-19.

For this, a new resource was incorporated into the team with bioinformatic knowledge and responsible for creating not only the algorithm and software, but also biometrics. It is important to note that we will continue post-project development with additional funds from the funding rounds currently being worked on.