Data Science Seminars: Bioinformatics focus

Speaker:  Keying Qiao

May 12^th, 2026 | 2:15 pm
DEIB, PT2 Meeting Room (Bld. 20A)

For further information please contact:  Silvia Cascianelli |  silvia.cascianelli@polimi.it

Abstract

Tuesday, May 12^th, 2026 at 2:15 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB PT2 Meeting Room (Building 20A) organized by the Data Science for Bioinformatics group.

The seminar will be held by Keying Qiao, PHD Student in Information Technology, on the following subject: "Multi-omics Analysis of Drug Response for Precision Therapy".

Cancer heterogeneity contributes to diverse therapeutic responses across patients and remains a major challenge in precision therapy. Because drug response is regulated by complex interactions across multiple molecular layers including the genome, transcriptome, proteome, and epigenome, single-omics analyses are often insufficient to comprehensively characterize these biological processes. In this seminar, I will present multi-omics approaches for drug-response analysis and precision treatment strategy discovery. First, we developed OncoMICS, a multi-omics analysis platform for cancer precision therapy. The platform stratifies samples based on pathological and molecular characteristics and associates transcriptomic, genomic, and proteomic data with drug sensitivity profiles to identify potential therapeutic targets and combination treatment strategies for specific cancer subgroups. Using KRAS-mutant non-small cell lung cancer as a case study, the platform identified several potential therapeutic targets and drug combinations, including the combination of Trametinib and Navitoclax. After predicting therapeutic strategies for stratified cancer subgroups, we further aimed to understand the underlying regulatory mechanisms of drug response. To achieve this, we constructed interpretable multi-omics drug-response networks through factor analysis–based integration of transcriptomic, genomic, proteomic, and epigenomic data using Multi-Omics Factor Analysis (MOFA). By identifying key factors associated with drug sensitivity and analyzing downstream network relationships, we explored the molecular mechanisms underlying drug response and further interpreted the rationale of the predicted combination therapies. Overall, this work combines multi-omics analysis, drug sensitivity association analysis, and network modeling to support drug-response interpretation and precision treatment strategy discovery.