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 https://www.synapse.org/#!Synapse:syn4231880/wiki/235664
2016 AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1b. Predict drug synergy using genomic information https://www.synapse.org/#!Synapse:syn4231880/wiki/235664
2016 AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 2. Predict drug synergy without drug combination training data (novel drug pairs) https://www.synapse.org/#!Synapse:syn4231880/wiki/235664
2016 ICGC-TCGA-DREAM Somatic Mutation Calling Challenge -- Tumor Heterogeneity and Evolution Inferring tumor heterogeneity and subclone reconstruction https://www.synapse.org/#!Synapse:syn6087005/wiki/398911
2015 ALS Stratification Prize4Life Challenge 1. Predict survival in ALS PROACT data https://www.synapse.org/#!Synapse:syn2873386/wiki/391432
2015 ALS Stratification Prize4Life Challenge 3. Predict survival for national registry data https://www.synapse.org/#!Synapse:syn2873386/wiki/391432
2015 Olfaction Challenge 1. Predict olfaction response for individuals using chemical structure data https://www.synapse.org/#!Synapse:syn2811262/wiki/78388
2015 Prostate Cancer Challenge 2. Predict which patients cannot tolerate chemotherapy https://www.synapse.org/#!Synapse:syn2813558/wiki/235080
2014 Rheumatoid Arthritis Challenge 1. Predict patient response to specific drugs https://www.synapse.org/#!Synapse:syn1734172/wiki/65262
2014 Rheumatoid Arthritis Challenge 2. Predict the patients that do not respond to treatment (non-responders) https://www.synapse.org/#!Synapse:syn1734172/wiki/65262
2014 Alzheimer BigData Challenge #1 1. Predict the prognosis of AD patients. https://www.synapse.org/#!Synapse:syn2290704/wiki/70719
2014 Alzheimer BigData Challenge #1 3. Classify individuals into diagnostic groups using MR imaging. https://www.synapse.org/#!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 http://support.sagebase.org/sagebase/topics/broad-dream-gene-essentialit...
2013 Breast Cancer Network Inference 2. Drug response of phosphorylation network prediction in cancer cells https://www.synapse.org/#!Synapse:syn1720047/wiki/60532