We are interested in machine learning, medical devices and drug discovery, please see GuanLab Research Summary, and summary 'The Art of Best-performing Algorithms'. This lab is featured in the inaugural issue of Nature Machine Intelligence in 2019.
Our team currently holds
1. the most accuracy algorithm in detecting Parkinson's Disease (winning algorithm 0.87 in AUC in 2017 DREAM PD Digital Biomarker Challenge vs. second place 0.70 in AUC) by mobile devices (iPhone, iWatch, Android, etc.).
2. the most accurate algorithm in detecting sleep apnea and arousal by ECG, EEG, accelometer, oxygen monitors (0.93 AUC in 2018 PhysioNet Challenge).
3. one of the two most accurate algorithms (tied in performance) in mammography reading (0.86 in AUC, winning algorithm in 2017 DREAM mammography Challenge).
All of our medical device algorithms are by deep learning.
Our team currently holds
1. the most accurate algorithm in predicting cancer drug synergy, reaching the accuracy level of experimental replicates (0.53 in correlation coefficient compared to experiments vs. 0.53 in correlation coefficient by experimental replicates, winning algorithm in DREAM AstraZeneca Drug Combination challenge).
2. The most accurate algorithm in predicting olfaction response by chemical structure, reaching the accuracy of double tests by individuals.
All of our drug research are by structure predictions.
THE SIDE DISHES
Our team has written best-performing algorithms and set the state-of-the-art for many other problems beyond device and drug. This includes problems involving transcription factor prediction (ENCODE DREAM), Biomarker selection, Patient Survival and Outcomes and many more. We have contributed the majority of the best-performing algorithms in DREAM challenges, the largest systems biology benchmark study. I am the sole recipient of the DREAM 'Consistent Best Technical Performer' award, and one of the very few people globally who own multiple gold medals in the annual Data Science Bowl by Kaggle, and the best performer of many other Machine Learning Competitions such as Physionet.
Beyond deep learning, we also contribute to the development of traditional machine learning, I am the inventor of GuanRank (on survival), adaptive GPR and several other algorithms that are often used as the reference algorithms in benchmark studies/challenges. Relevant algorithms have been published in leading journals such as Science, Nature Methods, Nature Communication, etc.
Positions in the broad area of AI in biomedicine are open in the Guan Lab for research track positions, postdoc and graduate students with computational, mathematics or engineering background. Proficiency in python (or C++/JAVA) is expected for all candidates; proficiency in English writing is expected for research track positions and postdocs. Familiarity with SWIFT and Raspberry Pi is a plus, but not required. Please kindly email cv and sample code to firstname.lastname@example.org for application.