This research focuses on the application of AI algorithms to address challenges in animal and human genomics, biomedical sciences, and food production. It integrates innovative approaches for generating anticancer and antibiotic molecules, analyzing Next-Generation Sequencing (NGS) data from diverse genomic sources, and exploring the role of probiotics in human and animal health, as well as in sustainable food production.
State-of-the-art methodologies, including Generative Adversarial Networks (GANs), Flow-based models, Diffusion models, and Large Language Models (LLMs), will be employed. The project will utilize programming frameworks such as Python, PyTorch, TensorFlow, and R for data analysis, model development, and validation.

The research will also focus on creating genomic databases and tools by parsing NCBI data, along with investigating genomic features such as Shannon entropy and Chargaff’ second parity rule. These efforts aim to provide novel computational solutions and contribute to advancements in artificial intelligence and genomics across diverse fields.

Five publications related to the Research Topic for the candidate interview:

1. Applications of machine learning in drug discovery and development, Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; et al. Nat. Rev. Drug Discov.2019, 18, 463–477.
2. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation, Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein Author Notes. Briefings in Bioinformatics, Volume 25, Issue 4, July 2024, bbae338, https://doi.org/10.1093/bib/bbae338
3. iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences, Yu Sun, Haicheng Li, Lei Zheng, Jinzhao Li, Yan Hong, Pengfei Liang, Lai-Yu Kwok, Yongchun Zuo, Wenyi Zhang, Heping Zhang, Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab477, DOI: https://doi.org/10.1093/bib/bbab477
4. Topological entropy of DNA sequences, David Koslicki, Bioinformatics, Volume 27, Issue 8, April 2011, Pages 1061–1067, https://doi.org/10.1093/bioinformatics/btr077
5. Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rule, P Pflughaupt, AB Sahakyan, Nucleic Acids Research, 51 (14), 7409-7423

Tutor: Prof. Cristian Taccioli
mail: cristian.taccioli@unipd.it