Identification of new chemotherapeutic compounds through machine-learning algorithms and validation in human and animal cancer cell lines

Short description:

Cancer is one of the most leading causes of death globally, and the use of antineoplastic molecules to target and kill cancer cells has received considerable attention worldwide. However, the identification of anticancer drugs through experimentation in wet laboratories is expensive and can be afforded only by few research groups. Thus, the development of an efficient computational method to identify specific compounds able to kill cancer cells is essential, prior to an in vitro and in vivo experimentation. Therefore, the PhD student candidate will learn how to develop and uses machine-learning method for the prediction of anticancer molecules and test these new compounds in vitro using human and animal cancer cell lines. Moreover, he will perform a comparison analysis between human and animal cancer cells using NGS (Next Generation Sequencing) techinques in order to identify the biological response mechanism of normal and cancer cells to candidate chemotherapeutics drugs.

Five publications related to the Research Topic for the interview:

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