|Title||Machine Learning in Computational Biology to Accelerate High-Throughput Protein Expression.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Sastry A, Monk J, Tegel H, Uhlen M, Palsson BO, Rockberg J, Brunk E|
Motivation: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40,000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecularlevel properties influencing expression and solubility.
Machine Learning in Computational Biology to Accelerate High-Throughput Protein Expression.