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Principal Scientist, Predictive Biology and Ai @Bristol Myers Squibb
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WORK HISTORY
Principal Scientist, Predictive Biology and Ai @Bristol Myers Squibb
Lawrenceville, NJ, US
1) Development of AI/LLM-driven method for prediction of drug perturbation outcome.2) Optimization of sequence design with sequence-to-function transformer model (enformer, alphagenome).3) Protein structure and binding affinity modeling for CAR (chimeric antigen receptor) construct screening and early development of cell therapy
EDUCATION
Virginia Tech
Doctor of Philosophy (Ph.D.), Genetics, Bioinformatics and Computational Biology
Fujian Agriculture and Forestry University
Master’s Degree, Bioinformatics
Fujian Agriculture and Forestry University
Bachelor’s Degree, Microbiology, General
SKILLS
ABOUT QI ALEX SONG
The power of data science and machine learning has accelerated the biology discovery and large-scale screening of gene candidates. Of note, characterizing the associations among genes can not only improve systematic understanding of disease associated pathways but also uncover novel candidate genes/biomarkers for therapies. My research focuses on developing machine learning based tool to unveil biological associations or applying/building computational tools to model single cell genomic data. Particularly, I have developed :1) a multi-task learning based framework to reconstruct gene regulatory relationship from single cell data (Nucleic Acids Research, https://academic.oup.com/nar/article/51/7/e38/70••••82) an unsupervised clustering tool for analyzing single cell data (Genome Biology, https://genomebiology.biomedcentral.com/articles/10.1186/s13••••••••93) drug discovery tool for Idiopathic Pulmonary Fibrosis (IPF).4) a machine learning based framework for reconstructing stress response regulatory networks (Nucleic Acids Research, https://academic.oup.com/nar/article/48/11/e62/58•••11)I have also been involved in several other single cell genomic projects that cover different themes:1) machine-learning based aging effect prediction with senescence gene markers (under review).2) probabilistic model based cell ordering for single cell genomic data.
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