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Date: Sun Jul 25 10:49:01 2021
Subject: 21.07.25 postdoc in AI + OMICS + Drug Discovery
The Bin Chen Laboratory ( uses big data and artificial 
intelligence to discover new or better therapeutic candidates. The lab has collected over 
30,000 bulk RNA-Seq profiles and millions of single-cell RNA-Seq profiles 
and drug-induced gene expression profiles. The lab is now capable of 
screening drug candidates from millions of novel compounds for diseases
 based on gene expression. Our postdoctoral fellow will help refine the 
models and translate these data points into therapeutics. The lab works 
on the following diseases: liver cancer, DIPG, melanoma, Alzheimers, and 
COVID-19 and is open to study other conditions. In addition, the successful
 candidate is expected to lead or assist with any of the following projects:
 (1) developing deep learning and transfer learning algorithms to discover 
novel therapeutics and transfer knowledge between preclinical and clinical 
model, (2) developing interpretable machine learning methods to predict 
drug-induced gene expression profiles, and (3) developing deep reinforcement
 learning models in lead optimization. The candidate will closely work with
 computer scientists to develop models and bench scientists to validate drug
 hits. The candidate could be strong in either medicinal chemistry or 
bioinformatics. The candidates who are interested in exploring AlphaFold are more
than welcome to apply. Recent Chen Lab work has been published in Gastroenterology,
 Nature Communications, Nature Protocols, Nature Reviews Gastroenterology 
and Hepatology, ICDM, and KDD and featured in STAT, GEN, GenomeWeb, and KCBS.

Papers related to this position:
Chen B#, Garmire L, Calvisi DF, Chua M-S, Kelley RK, Chen X. Harnessing big omics data and AI for drug discovery in hepatocellular carcinoma, Nat Rev Gastroenterol Hepatol. 2020 Jan 3. doi: 10.1038/s41575-019-0240-9. PMID: 31900465
#Billy Zeng, #Benjamin S. Glicksberg, #Patrick Newbury, #Evgenii Chekalin, Jing Xing, Ke Liu, Anita Wen, Caven Chow, Bin Chen, OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features, Nat Protoc., 2020 Dec 23. doi: 10.1038/s41596-020-00430-z. PMID:33361798
Mengying Sun, Jing Xing, Bin Chen, Jiayu Zhou, Robust Collaborative Learning with Noisy Labels, ICDM, 2020,link
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, Jiayu Zhou, MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge, KDD, 2021,link
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