Data Science Seminars - Omics Data Science and its applications in dissecting intratumor heterogeneity
Wednesday, May 7, 2025 | 5:00 p.m.
Data Science and Bioinformatics Lab (Building 21)
Dipartimento di Elettronica, Informazione e Bioingegneria - Politecnico di Milano
Speaker: Silvia Cascianelli (Politecnico di Milano)
Contacts: Silvia Cascianelli | silvia.cascianelli@polimi.it
Data Science and Bioinformatics Lab (Building 21)
Dipartimento di Elettronica, Informazione e Bioingegneria - Politecnico di Milano
Speaker: Silvia Cascianelli (Politecnico di Milano)
Contacts: Silvia Cascianelli | silvia.cascianelli@polimi.it
Abstract
Wednesday, May 7, 2025 at 5:00 p.m. Silvia Cascianelli (Politecnico di Milano) will hold a seminar titled "Omics Data Science and its applications in dissecting intratumor heterogeneity" in the Data Science and Bioinformatics Lab (Building 21). The event is part of the Data Science Seminars organized by the Data Science Lab at Politecnico di Milano.
This seminar offers an introduction to Omics Data Science, emphasizing its growing role in precision medicine, particularly within the oncogenomics field. The discussion will then focus on how Omics Data Science workflows and Machine Learning models can be leveraged for cancer subtyping and patient stratification, with the aim of dissecting intratumor heterogeneity. As a key application, the seminar will indeed explore the impact of multi-label classification strategies, which enable the identification of overlapping subtypes and molecular traits within individual tumor samples.
This seminar offers an introduction to Omics Data Science, emphasizing its growing role in precision medicine, particularly within the oncogenomics field. The discussion will then focus on how Omics Data Science workflows and Machine Learning models can be leveraged for cancer subtyping and patient stratification, with the aim of dissecting intratumor heterogeneity. As a key application, the seminar will indeed explore the impact of multi-label classification strategies, which enable the identification of overlapping subtypes and molecular traits within individual tumor samples.
