Guan Lab

Department of Computational Medicine & Bioinformatics

GuanLab wins 2014 DREAM RA Drug Responder challenge

The GuanLab team developed a novel data mining approach for predicting drug response using genetic and clinical information. This algorithm ranks top in the 2014 DREAM (Dialogue for Reverse Engineering Assessments and Methods) rheumatoid arthritis drug response challenge, for both genetics-only model and combined model, for both sub-challenges, and for both the leaderboard and the final previously unseen test set.

Guan Lab co-wins 2014 DREAM Broad DREAM Gene Essentiality Prediction Challenge

We developed a rigorous method for identifying predictive biomarkers for cancer cell survivability. This is the winning algorithm in the 2014 DREAM Broad DREAM Gene Essentiality Prediction Challenge - sub challenge 2: selecting the most predictive biomarkers for different cancer cell lines.

Guan Lab co-wins 2013 DREAM8 Network Inference Challenge

We developed an elegant model for predicting the rewiring of causal networks under unseen interventions. This model achieved the best aggregated performance (in accuracy) in the 2013 HPN-DREAM8 Breast Cancer Network Inference Sub-Challenge 2A and 2B: Time-course prediction. It was identified as 'most consistent performing' for this task, defined as ranked robustly first using random subsets for testing. Additionally, this model is orders of magnitudes faster compared to contemporary methods, realizing genome-wide causal network reconstruction and accurate drug response prediction in less than 30 seconds.
Differentiating functions at the Isoform level

Isoform-level functional genomic data integration

We developed a generic algorithm that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms and to model isoform-level networks. For a specific function, our algorithm identifies the ‘responsible’ isoform of a gene and generates classifying models at the isoform level instead of at the gene level.