Guan Lab

Department of Computational Medicine & Bioinformatics

I am interested in machine learning, network biology that is applied to clinical outcome prediction. The current machine learning field is dominated by gradient descent-based methods; I am more interested in developing the branch of techniques with matrix manipulation to complement the strength of gradient descent. I have written many top-performing algorithms in the systems biology field, as benchmarked by community-based challenges. Most of them involve a strong component of matrix decomposition, multiplication, or transformation. I consider the gradient descent-derived approach in machine learning today and the matrix-derived approach are the natural descendants of two branches of modern math: calculus and linear algebra, which together will deliver the most powerful and accurate prediction algorithms. I encourage Umich students to take advantage of the courses I am teaching to learn these top-performing algorithms.

I enjoy working with kids and found it a rewarding process to build long-term relationship with a smart student and a sweet family. I have coached high school students who placed first in the Southeast Michigan Science Fair, or entered Intel STS finalist. This is my personal blog.

I wrote the 1st place solutions for the following DREAM challenges: ALL CODE/DESCRIPTION ARE ACCESSIBLE HERE.

Year Challenge Sub-challenge Challenge webpage
2016 AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1a. Predict drug synergy using drug combinational training data, expression, CNV, mutation, drug chemical space!Synapse:syn4231880/wiki/235664
2016 AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1b. Predict drug synergy using genomic information!Synapse:syn4231880/wiki/235664
2016 AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 2. Predict drug synergy without drug combination training data (novel drug pairs)!Synapse:syn4231880/wiki/235664
2016 ICGC-TCGA-DREAM Somatic Mutation Calling Challenge -- Tumor Heterogeneity and Evolution Inferring tumor heterogeneity and subclone reconstruction!Synapse:syn6087005/wiki/398911
2015 ALS Stratification Prize4Life Challenge 1. Predict survival in ALS PROACT data!Synapse:syn2873386/wiki/391432
2015 ALS Stratification Prize4Life Challenge 3. Predict survival for national registry data!Synapse:syn2873386/wiki/391432
2015 Olfaction Challenge 1. Predict olfaction response for individuals using chemical structure data!Synapse:syn2811262/wiki/78388
2015 Prostate Cancer Challenge 2. Predict which patients cannot tolerate chemotherapy!Synapse:syn2813558/wiki/235080
2014 Rheumatoid Arthritis Challenge 1. Predict patient response to specific drugs!Synapse:syn1734172/wiki/65262
2014 Rheumatoid Arthritis Challenge 2. Predict the patients that do not respond to treatment (non-responders)!Synapse:syn1734172/wiki/65262
2014 Alzheimer BigData Challenge #1 1. Predict the prognosis of AD patients.!Synapse:syn2290704/wiki/70719
2014 Alzheimer BigData Challenge #1 3. Classify individuals into diagnostic groups using MR imaging.!Synapse:syn2290704/wiki/70719
2014 Broad Institute Gene Essentiality Challenge 2. Identify the most predictive biomarkers to predict cancer cell survivability under small molecule perturbations
2013 Breast Cancer Network Inference 2. Drug response of phosphorylation network prediction in cancer cells!Synapse:syn1720047/wiki/60532