Dr Kim-Anh Do (University of Texas)

We propose personalized cancer-specific integrated network estimation (PRECISE), a general framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures.

We develop a Bayesian regression model for protein-protein interactions that integrates with known pathway annotations and protein-protein interactions. In the first step of PRECISE, a data-driven protein causal network is estimated and combined with the prior information.

Other upstream molecular data are then integrated in three sequential steps to produce cancer-specific, patient-specific networks and pathway scores, which are subsequently used for tumor subtype classification and clinical outcome prediction.

Using the pan-cancer functional proteomic data on 32 cancer types from The Cancer Genome Atlas, we demonstrate the utility of PRECISE in inferring commonalities and differences in network biology across tumor lineages and in using patient-specific pathway-based signatures for robust tumor stratification and prediction.

(Joint work with Veerabhadran Baladandayuthapani and Min Jin Ha.)

About Statistics, modelling and operations research seminars

Students, staff and visitors to UQ are welcome to attend our regular seminars.

The events are jointly run by our Operations research and Statistics and probability research groups.

The Statistics, modelling and operations research (SMOR) Seminar series seeks to celebrate and disseminate research and developments across the broad spectrum of quantitative sciences. The SMOR series provides a platform for communication of both theoretical and practical developments, as well as interdisciplinary topics relating to applied mathematics and statistics.


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